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NeurIPS Board. Organizing Committee. Program Committee. NeurIPS Foundation. NeurIPS Thirty-fifth Annual Conference on Neural Information Processing Systems. NeurIPS 2021 is a Virtual-only Conference. Mon Dec 6th through Tue the 14th. (Monday is an industry expo) firstbacksecondback. 2021 DATES AND DEADLINES Oct 06 '21 (Anywhere on Earth) PaperSubmission. Abstract Submission Deadline. May 19 '21 08:00 PM UTC. 01 weeks 06 days 15:43:06. Papersubmission and co
NEURIPS 2021 CALL FOR PAPERS Call For Papers Abstract submission deadline: Wednesday, May 19, 2021 01:00 PM PDT Friday, May 21, 2021 01:00 PM PDT Full paper submission and co-author registration deadline: Wednesday, May 26, 2021 01:00 PM PDT Friday, May 28, 2021 01:00 PM PDT Supplementary material submission deadline: Wednesday, June 2, 2021 01:00 PM PDT Friday, June 4, 2021 01:00 PM PDT NEURIPS 2021 PAPER FAQ NeurIPS 2021 FAQ for Authors. We will update this page as new questions arise. Please check back regularly. If you do not find an answer to your question here, you are welcome to contact the NeurIPS 2021 program chairs at neurips2021pcs@gmail.com, but please make sure that you have read the call for papers and this document first..Submission process
WHAT UNCERTAINTIES DO WE NEED IN BAYESIAN DEEP LEARNING 2Related Work Existing approaches to Bayesian deep learning capture either epistemic uncertainty alone, or aleatoric uncertainty alone . These uncertainties are formalised as probability distributionsover
META-WEIGHT-NET: LEARNING AN EXPLICIT MAPPING FOR SAMPLE Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu 1, Qi Xie , Lixuan Yi , Qian Zhao , Sanping Zhou 1, Zongben Xu , and Deyu Meng*2,1 1Xi’an Jiaotong University 2The Macau University of Science and Technology *Corresponding author:dymeng@mail.xjtu.edu.cn Abstract Current deep neural networks (DNNs) can easily overfit to biased training data with GENERATIVE MODELING BY ESTIMATING GRADIENTS OF THE DATA distribution q ˙(x~ jx) and then employs score matching to estimate the score of the perturbed data distribution q ˙(x~) , R q ˙(~x jx)p data(x)dx.The objective was proved equivalent to the following: 1 2 E q ˙(~xjx)p data(x): (2) As shown in DEPTH MAP PREDICTION FROM A SINGLE IMAGE USING A MULTI 9x9 conv Concatenate 2 stride 2x2 pool 11x11 conv 4 stride 2x2 pool Fine 1 Coarse 1 5x5 conv 2x2 pool Coarse 2 96 64 Coarse 5 256 256 Coarse 6 4096 63 384 ATTENTION IS ALL YOU NEED Attention Is All You Need Ashish Vaswani Google Brain avaswani@google.com Noam Shazeer Google Brain noam@google.com Niki Parmar Google Research nikip@google.com IMAGE DENOISING AND INPAINTING WITH DEEP NEURAL NETWORKS Abstract. We present a novel approach to low-level vision problems that combines sparse coding and deep networks pre-trained with denoising auto-encoder (DA). We propose an alternative training scheme that successfully adapts DA, originally designed for unsupervised feature learning, to the tasks of image denoising and blindinpainting.
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NeurIPS Board. Organizing Committee. Program Committee. NeurIPS Foundation. NeurIPS Thirty-fifth Annual Conference on Neural Information Processing Systems. NeurIPS 2021 is a Virtual-only Conference. Mon Dec 6th through Tue the 14th. (Monday is an industry expo) firstbacksecondback. 2021 DATES AND DEADLINES Oct 06 '21 (Anywhere on Earth) PaperSubmission. Abstract Submission Deadline. May 19 '21 08:00 PM UTC. 01 weeks 06 days 15:43:06. Papersubmission and co
NEURIPS 2021 CALL FOR PAPERS Call For Papers Abstract submission deadline: Wednesday, May 19, 2021 01:00 PM PDT Friday, May 21, 2021 01:00 PM PDT Full paper submission and co-author registration deadline: Wednesday, May 26, 2021 01:00 PM PDT Friday, May 28, 2021 01:00 PM PDT Supplementary material submission deadline: Wednesday, June 2, 2021 01:00 PM PDT Friday, June 4, 2021 01:00 PM PDT NEURIPS 2021 PAPER FAQ NeurIPS 2021 FAQ for Authors. We will update this page as new questions arise. Please check back regularly. If you do not find an answer to your question here, you are welcome to contact the NeurIPS 2021 program chairs at neurips2021pcs@gmail.com, but please make sure that you have read the call for papers and this document first..Submission process
META-WEIGHT-NET: LEARNING AN EXPLICIT MAPPING FOR SAMPLE Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu 1, Qi Xie , Lixuan Yi , Qian Zhao , Sanping Zhou 1, Zongben Xu , and Deyu Meng*2,1 1Xi’an Jiaotong University 2The Macau University of Science and Technology *Corresponding author:dymeng@mail.xjtu.edu.cn Abstract Current deep neural networks (DNNs) can easily overfit to biased training data with WHAT UNCERTAINTIES DO WE NEED IN BAYESIAN DEEP LEARNING 2Related Work Existing approaches to Bayesian deep learning capture either epistemic uncertainty alone, or aleatoric uncertainty alone . These uncertainties are formalised as probability distributionsover
GENERATIVE MODELING BY ESTIMATING GRADIENTS OF THE DATA distribution q ˙(x~ jx) and then employs score matching to estimate the score of the perturbed data distribution q ˙(x~) , R q ˙(~x jx)p data(x)dx.The objective was proved equivalent to the following: 1 2 E q ˙(~xjx)p data(x): (2) As shown in DEPTH MAP PREDICTION FROM A SINGLE IMAGE USING A MULTI 9x9 conv Concatenate 2 stride 2x2 pool 11x11 conv 4 stride 2x2 pool Fine 1 Coarse 1 5x5 conv 2x2 pool Coarse 2 96 64 Coarse 5 256 256 Coarse 6 4096 63 384 ATTENTION IS ALL YOU NEED Attention Is All You Need Ashish Vaswani Google Brain avaswani@google.com Noam Shazeer Google Brain noam@google.com Niki Parmar Google Research nikip@google.com IMAGE DENOISING AND INPAINTING WITH DEEP NEURAL NETWORKS Abstract. We present a novel approach to low-level vision problems that combines sparse coding and deep networks pre-trained with denoising auto-encoder (DA). We propose an alternative training scheme that successfully adapts DA, originally designed for unsupervised feature learning, to the tasks of image denoising and blindinpainting.
CROSSTRANSFORMERS: SPATIALLY-AWARE FEW-SHOT TRANSFER CrossTransformers: spatially-aware few-shot transfer Carl Doersch Ankush Gupta Andrew Zissermany DeepMind, London yVGG, Department of Engineering Science, University of Oxford Abstract Given new tasks with very little data—such as new classes in a classification DENOISED SMOOTHING: A PROVABLE DEFENSE FOR PRETRAINED Our approach applies to both the white-box and the black-box settings of the pretrained classifier. We refer to this defense as denoised smoothing, and we demonstrate its effectiveness through extensive experimentation on ImageNet and CIFAR-10. Finally, we use our approach to provably defend the Azure, Google, AWS, and ClarifAI image INVESTIGATING GENDER BIAS IN LANGUAGE MODELS USING CAUSAL Investigating Gender Bias in Language Models Using Causal Mediation Analysis Jesse Vig 1 Sebastian Gehrmann *2Yonatan Belinkov Sharon Qian 2Daniel Nevo3 Yaron Singer Stuart Shieber2 1 Salesforce Research 2 Harvard University 3 Tel Aviv University jvig@salesforce.com danielnevo@tauex.tau.ac.il LEARNING DIVERSE AND DISCRIMINATIVE REPRESENTATIONS VIA f(x; ) RD Rd M M 1 M 2 M j x i S 1 S 2 S j z i Figure 1: Left and Middle: The distribution D of high-dim data x 2 RD is supported on a manifold M and its classes on low-dim submanifolds M j, we learn a map f(x; ) such that z i = f(x i; ) are on a union of maximally uncorrelated subspaces fS jg.Right: Cosine similarity between learned features by our method BOOSTING ADVERSARIAL TRAINING WITH HYPERSPHERE EMBEDDING softmax function. One common training objective for DNNs is the cross-entropy (CE) loss defined as L CE(f(x);y) = 1> y logf(x); (2) where 1 y is the one-hot encoding of label yand the logarithm of a vector is taken element-wisely. In this paper, we use \(u;v) to denote the angle between vectors uand v. GENERATIVE MODELING BY ESTIMATING GRADIENTS OF THE DATA distribution q ˙(x~ jx) and then employs score matching to estimate the score of the perturbed data distribution q ˙(x~) , R q ˙(~x jx)p data(x)dx.The objective was proved equivalent to the following: 1 2 E q ˙(~xjx)p data(x): (2) As shown in ON THE CONVERGENCE AND ROBUSTNESS OF TRAINING GANS WITH formulation based on the dual form of the resulting optimal transport problem. In this game-representation, the discriminator is comprised of a 1-Lipschitz function and aims at differentiating UNSUPERVISED IMAGE-TO-IMAGE TRANSLATION NETWORKS z ! h % x 1 & x 2. (1) Consequently, we have G ⇤ 1 ⌘ G⇤ L,1 ⇤GH and G 2 ⌘ G L,2 G H where G H is a common high-level generation function that maps z to h and G⇤ L,1 and G L,2 are low-level generation functions that map h to x 1 and x 2, respectively.In the case VARIATIONAL BAYESIAN MONTE CARLO Algorithm 1 Variational Bayesian Monte Carlo Input: target log joint f, starting point x 0, plausible bounds PLB , PUB , additional options 1: Initialization: t 0, initialize variational posterior ˚ 0, STOPSAMPLING false 2: repeat 3: t+1 4: if t, 1 then .Initial design, Section 3.5 5: Evaluate y 0 f (x 0)and add 0;y 0 to the training set 6: for 2:::n init do 7: Sample a new point x SEQUENCE TO SEQUENCE LEARNING WITH NEURAL NETWORKS Abstract. Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes2021 CONFERENCE
NeurIPS Board. Organizing Committee. Program Committee. NeurIPS Foundation. NeurIPS Thirty-fifth Annual Conference on Neural Information Processing Systems. NeurIPS 2021 is a Virtual-only Conference. Mon Dec 6th through Tue the 14th. (Monday is an industry expo) firstbacksecondback. 2021 DATES AND DEADLINES Oct 06 '21 (Anywhere on Earth) PaperSubmission. Abstract Submission Deadline. May 19 '21 08:00 PM UTC. 01 weeks 06 days 15:43:06. Papersubmission and co
NEURIPS 2021 PAPER FAQ NeurIPS 2021 FAQ for Authors. We will update this page as new questions arise. Please check back regularly. If you do not find an answer to your question here, you are welcome to contact the NeurIPS 2021 program chairs at neurips2021pcs@gmail.com, but please make sure that you have read the call for papers and this document first..Submission process
NEURIPS 2021 CALL FOR PAPERS Call For Papers Abstract submission deadline: Wednesday, May 19, 2021 01:00 PM PDT Friday, May 21, 2021 01:00 PM PDT Full paper submission and co-author registration deadline: Wednesday, May 26, 2021 01:00 PM PDT Friday, May 28, 2021 01:00 PM PDT Supplementary material submission deadline: Wednesday, June 2, 2021 01:00 PM PDT Friday, June 4, 2021 01:00 PM PDT MONOTONE OPERATOR EQUILIBRIUM NETWORKS In this paper, we develop a new class of implicit-depth model based on the theory of monotone operators, the Monotone Operator Equilibrium Network (monDEQ). We show the close connection between finding the equilibrium point of an implicit network and solving a form of monotone operator splitting problem, which admits efficient solverswith
PREDRNN: RECURRENT NEURAL NETWORKS FOR PREDICTIVE LEARNING PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang School of Software Tsinghua University wangyb15@mails.tsinghua.edu.cn WHAT UNCERTAINTIES DO WE NEED IN BAYESIAN DEEP LEARNING 2Related Work Existing approaches to Bayesian deep learning capture either epistemic uncertainty alone, or aleatoric uncertainty alone . These uncertainties are formalised as probability distributionsover
DEPTH MAP PREDICTION FROM A SINGLE IMAGE USING A MULTI 9x9 conv Concatenate 2 stride 2x2 pool 11x11 conv 4 stride 2x2 pool Fine 1 Coarse 1 5x5 conv 2x2 pool Coarse 2 96 64 Coarse 5 256 256 Coarse 6 4096 63 384 META-WEIGHT-NET: LEARNING AN EXPLICIT MAPPING FOR SAMPLE Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu 1, Qi Xie , Lixuan Yi , Qian Zhao , Sanping Zhou 1, Zongben Xu , and Deyu Meng*2,1 1Xi’an Jiaotong University 2The Macau University of Science and Technology *Corresponding author:dymeng@mail.xjtu.edu.cn Abstract Current deep neural networks (DNNs) can easily overfit to biased training data with NON-LOCAL RECURRENT NETWORK FOR IMAGE RESTORATION Non-Local Recurrent Network for Image Restoration Ding Liu 1, Bihan Wen , Yuchen Fan , Chen Change Loy2, Thomas S. Huang1 1University of Illinois at Urbana-Champaign 2Nanyang Technological University {dingliu2, bwen3, yuchenf4, t-huang1}@illinois.edu ccloy@ntu.edu.sg Abstract Many classic methods have shown non-local self-similarity innatural images
2021 CONFERENCE
NeurIPS Board. Organizing Committee. Program Committee. NeurIPS Foundation. NeurIPS Thirty-fifth Annual Conference on Neural Information Processing Systems. NeurIPS 2021 is a Virtual-only Conference. Mon Dec 6th through Tue the 14th. (Monday is an industry expo) firstbacksecondback. 2021 DATES AND DEADLINES Oct 06 '21 (Anywhere on Earth) PaperSubmission. Abstract Submission Deadline. May 19 '21 08:00 PM UTC. 01 weeks 06 days 15:43:06. Papersubmission and co
NEURIPS 2021 PAPER FAQ NeurIPS 2021 FAQ for Authors. We will update this page as new questions arise. Please check back regularly. If you do not find an answer to your question here, you are welcome to contact the NeurIPS 2021 program chairs at neurips2021pcs@gmail.com, but please make sure that you have read the call for papers and this document first..Submission process
NEURIPS 2021 CALL FOR PAPERS Call For Papers Abstract submission deadline: Wednesday, May 19, 2021 01:00 PM PDT Friday, May 21, 2021 01:00 PM PDT Full paper submission and co-author registration deadline: Wednesday, May 26, 2021 01:00 PM PDT Friday, May 28, 2021 01:00 PM PDT Supplementary material submission deadline: Wednesday, June 2, 2021 01:00 PM PDT Friday, June 4, 2021 01:00 PM PDT MONOTONE OPERATOR EQUILIBRIUM NETWORKS In this paper, we develop a new class of implicit-depth model based on the theory of monotone operators, the Monotone Operator Equilibrium Network (monDEQ). We show the close connection between finding the equilibrium point of an implicit network and solving a form of monotone operator splitting problem, which admits efficient solverswith
PREDRNN: RECURRENT NEURAL NETWORKS FOR PREDICTIVE LEARNING PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang School of Software Tsinghua University wangyb15@mails.tsinghua.edu.cn WHAT UNCERTAINTIES DO WE NEED IN BAYESIAN DEEP LEARNING 2Related Work Existing approaches to Bayesian deep learning capture either epistemic uncertainty alone, or aleatoric uncertainty alone . These uncertainties are formalised as probability distributionsover
DEPTH MAP PREDICTION FROM A SINGLE IMAGE USING A MULTI 9x9 conv Concatenate 2 stride 2x2 pool 11x11 conv 4 stride 2x2 pool Fine 1 Coarse 1 5x5 conv 2x2 pool Coarse 2 96 64 Coarse 5 256 256 Coarse 6 4096 63 384 META-WEIGHT-NET: LEARNING AN EXPLICIT MAPPING FOR SAMPLE Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu 1, Qi Xie , Lixuan Yi , Qian Zhao , Sanping Zhou 1, Zongben Xu , and Deyu Meng*2,1 1Xi’an Jiaotong University 2The Macau University of Science and Technology *Corresponding author:dymeng@mail.xjtu.edu.cn Abstract Current deep neural networks (DNNs) can easily overfit to biased training data with NON-LOCAL RECURRENT NETWORK FOR IMAGE RESTORATION Non-Local Recurrent Network for Image Restoration Ding Liu 1, Bihan Wen , Yuchen Fan , Chen Change Loy2, Thomas S. Huang1 1University of Illinois at Urbana-Champaign 2Nanyang Technological University {dingliu2, bwen3, yuchenf4, t-huang1}@illinois.edu ccloy@ntu.edu.sg Abstract Many classic methods have shown non-local self-similarity innatural images
DENOISED SMOOTHING: A PROVABLE DEFENSE FOR PRETRAINED Our approach applies to both the white-box and the black-box settings of the pretrained classifier. We refer to this defense as denoised smoothing, and we demonstrate its effectiveness through extensive experimentation on ImageNet and CIFAR-10. Finally, we use our approach to provably defend the Azure, Google, AWS, and ClarifAI image GRADIENT SURGERY FOR MULTI-TASK LEARNING Gradient Surgery for Multi-Task Learning Tianhe Yu 1, Saurabh Kumar , Abhishek Gupta2, Sergey Levine2, Karol Hausman3, Chelsea Finn1 Stanford University1, UC Berkeley2, Robotics at Google3 tianheyu@cs.stanford.edu Abstract While deep learning and deep INVESTIGATING GENDER BIAS IN LANGUAGE MODELS USING CAUSAL Investigating Gender Bias in Language Models Using Causal Mediation Analysis Jesse Vig 1 Sebastian Gehrmann *2Yonatan Belinkov Sharon Qian 2Daniel Nevo3 Yaron Singer Stuart Shieber2 1 Salesforce Research 2 Harvard University 3 Tel Aviv University jvig@salesforce.com danielnevo@tauex.tau.ac.il PIXEL-LEVEL CYCLE ASSOCIATION: A NEW PERSPECTIVE FOR Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation Guoliang Kang1, Yunchao Wei 2, Yi Yang , Yueting Zhuang3, Alexander G. Hauptmann1 1 School of Computer Science, Carnegie Mellon University 2 ReLER, University of Technology Sydney 3 Zhejiang University kgl.prml@gmail.com, alex@cs.cmu.edu {yunchao.wei,yi.yang}@uts.edu.au
CROSSTRANSFORMERS: SPATIALLY-AWARE FEW-SHOT TRANSFER CrossTransformers: spatially-aware few-shot transfer Carl Doersch Ankush Gupta Andrew Zissermany DeepMind, London yVGG, Department of Engineering Science, University of Oxford Abstract Given new tasks with very little data—such as new classes in a classification FALCON: FAST SPECTRAL INFERENCE ON ENCRYPTED DATA client. A non-interactive HE-enabled NN is a practical MLaaS solution with competitive accuracy for particular clients who have limited computing power and small network bandwidth. DEEP NETWORK FOR THE INTEGRATED 3D SENSING OF MULTIPLE Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images Andrei Zanfir, Elisabeta Marinoiu, Mihai Zanfir, Alin-Ionut Popa, and DEEP LEARNING FOR PRECIPITATION NOWCASTING: A BENCHMARK Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung Department of Computer Science and Engineering VARIATIONAL BAYESIAN MONTE CARLO Algorithm 1 Variational Bayesian Monte Carlo Input: target log joint f, starting point x 0, plausible bounds PLB , PUB , additional options 1: Initialization: t 0, initialize variational posterior ˚ 0, STOPSAMPLING false 2: repeat 3: t+1 4: if t, 1 then .Initial design, Section 3.5 5: Evaluate y 0 f (x 0)and add 0;y 0 to the training set 6: for 2:::n init do 7: Sample a new point x END-TO-END SYMMETRY PRESERVING INTER-ATOMIC POTENTIAL End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems Linfeng Zhang 1, Jiequn Han , Han Wang2 ;3, Wissam A. Saidi4;y, Roberto Car 1 ;5 6, Weinan E 7 8 z 1 Program in Applied and Computational Mathematics, Princeton University, USA 2 Institute of Applied Physics and Computational Mathematics, China 3 CAEP Software Center for High Performance2021 CONFERENCE
NeurIPS Board. Organizing Committee. Program Committee. NeurIPS Foundation. NeurIPS Thirty-fifth Annual Conference on Neural Information Processing Systems. NeurIPS 2021 is a Virtual-only Conference. Mon Dec 6th through Tue the 14th. (Monday is an industry expo) firstbacksecondback. 2021 DATES AND DEADLINES Oct 06 '21 (Anywhere on Earth) PaperSubmission. Abstract Submission Deadline. May 19 '21 08:00 PM UTC. 01 weeks 06 days 15:43:06. Papersubmission and co
NEURIPS 2021 PAPER FAQ NeurIPS 2021 FAQ for Authors. We will update this page as new questions arise. Please check back regularly. If you do not find an answer to your question here, you are welcome to contact the NeurIPS 2021 program chairs at neurips2021pcs@gmail.com, but please make sure that you have read the call for papers and this document first..Submission process
NEURIPS 2021 CALL FOR PAPERS Call For Papers Abstract submission deadline: Wednesday, May 19, 2021 01:00 PM PDT Friday, May 21, 2021 01:00 PM PDT Full paper submission and co-author registration deadline: Wednesday, May 26, 2021 01:00 PM PDT Friday, May 28, 2021 01:00 PM PDT Supplementary material submission deadline: Wednesday, June 2, 2021 01:00 PM PDT Friday, June 4, 2021 01:00 PM PDT MONOTONE OPERATOR EQUILIBRIUM NETWORKS In this paper, we develop a new class of implicit-depth model based on the theory of monotone operators, the Monotone Operator Equilibrium Network (monDEQ). We show the close connection between finding the equilibrium point of an implicit network and solving a form of monotone operator splitting problem, which admits efficient solverswith
PREDRNN: RECURRENT NEURAL NETWORKS FOR PREDICTIVE LEARNING PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang School of Software Tsinghua University wangyb15@mails.tsinghua.edu.cn WHAT UNCERTAINTIES DO WE NEED IN BAYESIAN DEEP LEARNING 2Related Work Existing approaches to Bayesian deep learning capture either epistemic uncertainty alone, or aleatoric uncertainty alone . These uncertainties are formalised as probability distributionsover
DEPTH MAP PREDICTION FROM A SINGLE IMAGE USING A MULTI 9x9 conv Concatenate 2 stride 2x2 pool 11x11 conv 4 stride 2x2 pool Fine 1 Coarse 1 5x5 conv 2x2 pool Coarse 2 96 64 Coarse 5 256 256 Coarse 6 4096 63 384 META-WEIGHT-NET: LEARNING AN EXPLICIT MAPPING FOR SAMPLE Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu 1, Qi Xie , Lixuan Yi , Qian Zhao , Sanping Zhou 1, Zongben Xu , and Deyu Meng*2,1 1Xi’an Jiaotong University 2The Macau University of Science and Technology *Corresponding author:dymeng@mail.xjtu.edu.cn Abstract Current deep neural networks (DNNs) can easily overfit to biased training data with NON-LOCAL RECURRENT NETWORK FOR IMAGE RESTORATION Non-Local Recurrent Network for Image Restoration Ding Liu 1, Bihan Wen , Yuchen Fan , Chen Change Loy2, Thomas S. Huang1 1University of Illinois at Urbana-Champaign 2Nanyang Technological University {dingliu2, bwen3, yuchenf4, t-huang1}@illinois.edu ccloy@ntu.edu.sg Abstract Many classic methods have shown non-local self-similarity innatural images
2021 CONFERENCE
NeurIPS Board. Organizing Committee. Program Committee. NeurIPS Foundation. NeurIPS Thirty-fifth Annual Conference on Neural Information Processing Systems. NeurIPS 2021 is a Virtual-only Conference. Mon Dec 6th through Tue the 14th. (Monday is an industry expo) firstbacksecondback. 2021 DATES AND DEADLINES Oct 06 '21 (Anywhere on Earth) PaperSubmission. Abstract Submission Deadline. May 19 '21 08:00 PM UTC. 01 weeks 06 days 15:43:06. Papersubmission and co
NEURIPS 2021 PAPER FAQ NeurIPS 2021 FAQ for Authors. We will update this page as new questions arise. Please check back regularly. If you do not find an answer to your question here, you are welcome to contact the NeurIPS 2021 program chairs at neurips2021pcs@gmail.com, but please make sure that you have read the call for papers and this document first..Submission process
NEURIPS 2021 CALL FOR PAPERS Call For Papers Abstract submission deadline: Wednesday, May 19, 2021 01:00 PM PDT Friday, May 21, 2021 01:00 PM PDT Full paper submission and co-author registration deadline: Wednesday, May 26, 2021 01:00 PM PDT Friday, May 28, 2021 01:00 PM PDT Supplementary material submission deadline: Wednesday, June 2, 2021 01:00 PM PDT Friday, June 4, 2021 01:00 PM PDT MONOTONE OPERATOR EQUILIBRIUM NETWORKS In this paper, we develop a new class of implicit-depth model based on the theory of monotone operators, the Monotone Operator Equilibrium Network (monDEQ). We show the close connection between finding the equilibrium point of an implicit network and solving a form of monotone operator splitting problem, which admits efficient solverswith
PREDRNN: RECURRENT NEURAL NETWORKS FOR PREDICTIVE LEARNING PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang School of Software Tsinghua University wangyb15@mails.tsinghua.edu.cn WHAT UNCERTAINTIES DO WE NEED IN BAYESIAN DEEP LEARNING 2Related Work Existing approaches to Bayesian deep learning capture either epistemic uncertainty alone, or aleatoric uncertainty alone . These uncertainties are formalised as probability distributionsover
DEPTH MAP PREDICTION FROM A SINGLE IMAGE USING A MULTI 9x9 conv Concatenate 2 stride 2x2 pool 11x11 conv 4 stride 2x2 pool Fine 1 Coarse 1 5x5 conv 2x2 pool Coarse 2 96 64 Coarse 5 256 256 Coarse 6 4096 63 384 META-WEIGHT-NET: LEARNING AN EXPLICIT MAPPING FOR SAMPLE Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu 1, Qi Xie , Lixuan Yi , Qian Zhao , Sanping Zhou 1, Zongben Xu , and Deyu Meng*2,1 1Xi’an Jiaotong University 2The Macau University of Science and Technology *Corresponding author:dymeng@mail.xjtu.edu.cn Abstract Current deep neural networks (DNNs) can easily overfit to biased training data with NON-LOCAL RECURRENT NETWORK FOR IMAGE RESTORATION Non-Local Recurrent Network for Image Restoration Ding Liu 1, Bihan Wen , Yuchen Fan , Chen Change Loy2, Thomas S. Huang1 1University of Illinois at Urbana-Champaign 2Nanyang Technological University {dingliu2, bwen3, yuchenf4, t-huang1}@illinois.edu ccloy@ntu.edu.sg Abstract Many classic methods have shown non-local self-similarity innatural images
DENOISED SMOOTHING: A PROVABLE DEFENSE FOR PRETRAINED Our approach applies to both the white-box and the black-box settings of the pretrained classifier. We refer to this defense as denoised smoothing, and we demonstrate its effectiveness through extensive experimentation on ImageNet and CIFAR-10. Finally, we use our approach to provably defend the Azure, Google, AWS, and ClarifAI image GRADIENT SURGERY FOR MULTI-TASK LEARNING Gradient Surgery for Multi-Task Learning Tianhe Yu 1, Saurabh Kumar , Abhishek Gupta2, Sergey Levine2, Karol Hausman3, Chelsea Finn1 Stanford University1, UC Berkeley2, Robotics at Google3 tianheyu@cs.stanford.edu Abstract While deep learning and deep INVESTIGATING GENDER BIAS IN LANGUAGE MODELS USING CAUSAL Investigating Gender Bias in Language Models Using Causal Mediation Analysis Jesse Vig 1 Sebastian Gehrmann *2Yonatan Belinkov Sharon Qian 2Daniel Nevo3 Yaron Singer Stuart Shieber2 1 Salesforce Research 2 Harvard University 3 Tel Aviv University jvig@salesforce.com danielnevo@tauex.tau.ac.il PIXEL-LEVEL CYCLE ASSOCIATION: A NEW PERSPECTIVE FOR Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation Guoliang Kang1, Yunchao Wei 2, Yi Yang , Yueting Zhuang3, Alexander G. Hauptmann1 1 School of Computer Science, Carnegie Mellon University 2 ReLER, University of Technology Sydney 3 Zhejiang University kgl.prml@gmail.com, alex@cs.cmu.edu {yunchao.wei,yi.yang}@uts.edu.au
CROSSTRANSFORMERS: SPATIALLY-AWARE FEW-SHOT TRANSFER CrossTransformers: spatially-aware few-shot transfer Carl Doersch Ankush Gupta Andrew Zissermany DeepMind, London yVGG, Department of Engineering Science, University of Oxford Abstract Given new tasks with very little data—such as new classes in a classification FALCON: FAST SPECTRAL INFERENCE ON ENCRYPTED DATA client. A non-interactive HE-enabled NN is a practical MLaaS solution with competitive accuracy for particular clients who have limited computing power and small network bandwidth. DEEP NETWORK FOR THE INTEGRATED 3D SENSING OF MULTIPLE Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images Andrei Zanfir, Elisabeta Marinoiu, Mihai Zanfir, Alin-Ionut Popa, and DEEP LEARNING FOR PRECIPITATION NOWCASTING: A BENCHMARK Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung Department of Computer Science and Engineering VARIATIONAL BAYESIAN MONTE CARLO Algorithm 1 Variational Bayesian Monte Carlo Input: target log joint f, starting point x 0, plausible bounds PLB , PUB , additional options 1: Initialization: t 0, initialize variational posterior ˚ 0, STOPSAMPLING false 2: repeat 3: t+1 4: if t, 1 then .Initial design, Section 3.5 5: Evaluate y 0 f (x 0)and add 0;y 0 to the training set 6: for 2:::n init do 7: Sample a new point x END-TO-END SYMMETRY PRESERVING INTER-ATOMIC POTENTIAL End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems Linfeng Zhang 1, Jiequn Han , Han Wang2 ;3, Wissam A. Saidi4;y, Roberto Car 1 ;5 6, Weinan E 7 8 z 1 Program in Applied and Computational Mathematics, Princeton University, USA 2 Institute of Applied Physics and Computational Mathematics, China 3 CAEP Software Center for High Performance2021 CONFERENCE
NeurIPS Board. Organizing Committee. Program Committee. NeurIPS Foundation. NeurIPS Thirty-fifth Annual Conference on Neural Information Processing Systems. NeurIPS 2021 is a Virtual-only Conference. Mon Dec 6th through Tue the 14th. (Monday is an industry expo) firstbacksecondback. 2021 DATES AND DEADLINES Oct 06 '21 (Anywhere on Earth) PaperSubmission. Abstract Submission Deadline. May 19 '21 08:00 PM UTC. 01 weeks 06 days 15:43:06. Papersubmission and co
NEURIPS 2021 PAPER FAQ NeurIPS 2021 FAQ for Authors. We will update this page as new questions arise. Please check back regularly. If you do not find an answer to your question here, you are welcome to contact the NeurIPS 2021 program chairs at neurips2021pcs@gmail.com, but please make sure that you have read the call for papers and this document first..Submission process
NEURIPS 2021 CALL FOR PAPERS Call For Papers Abstract submission deadline: Wednesday, May 19, 2021 01:00 PM PDT Friday, May 21, 2021 01:00 PM PDT Full paper submission and co-author registration deadline: Wednesday, May 26, 2021 01:00 PM PDT Friday, May 28, 2021 01:00 PM PDT Supplementary material submission deadline: Wednesday, June 2, 2021 01:00 PM PDT Friday, June 4, 2021 01:00 PM PDT MONOTONE OPERATOR EQUILIBRIUM NETWORKS In this paper, we develop a new class of implicit-depth model based on the theory of monotone operators, the Monotone Operator Equilibrium Network (monDEQ). We show the close connection between finding the equilibrium point of an implicit network and solving a form of monotone operator splitting problem, which admits efficient solverswith
PREDRNN: RECURRENT NEURAL NETWORKS FOR PREDICTIVE LEARNING PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang School of Software Tsinghua University wangyb15@mails.tsinghua.edu.cn WHAT UNCERTAINTIES DO WE NEED IN BAYESIAN DEEP LEARNING 2Related Work Existing approaches to Bayesian deep learning capture either epistemic uncertainty alone, or aleatoric uncertainty alone . These uncertainties are formalised as probability distributionsover
DEPTH MAP PREDICTION FROM A SINGLE IMAGE USING A MULTI 9x9 conv Concatenate 2 stride 2x2 pool 11x11 conv 4 stride 2x2 pool Fine 1 Coarse 1 5x5 conv 2x2 pool Coarse 2 96 64 Coarse 5 256 256 Coarse 6 4096 63 384 META-WEIGHT-NET: LEARNING AN EXPLICIT MAPPING FOR SAMPLE Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu 1, Qi Xie , Lixuan Yi , Qian Zhao , Sanping Zhou 1, Zongben Xu , and Deyu Meng*2,1 1Xi’an Jiaotong University 2The Macau University of Science and Technology *Corresponding author:dymeng@mail.xjtu.edu.cn Abstract Current deep neural networks (DNNs) can easily overfit to biased training data with NON-LOCAL RECURRENT NETWORK FOR IMAGE RESTORATION Non-Local Recurrent Network for Image Restoration Ding Liu 1, Bihan Wen , Yuchen Fan , Chen Change Loy2, Thomas S. Huang1 1University of Illinois at Urbana-Champaign 2Nanyang Technological University {dingliu2, bwen3, yuchenf4, t-huang1}@illinois.edu ccloy@ntu.edu.sg Abstract Many classic methods have shown non-local self-similarity innatural images
2021 CONFERENCE
NeurIPS Board. Organizing Committee. Program Committee. NeurIPS Foundation. NeurIPS Thirty-fifth Annual Conference on Neural Information Processing Systems. NeurIPS 2021 is a Virtual-only Conference. Mon Dec 6th through Tue the 14th. (Monday is an industry expo) firstbacksecondback. 2021 DATES AND DEADLINES Oct 06 '21 (Anywhere on Earth) PaperSubmission. Abstract Submission Deadline. May 19 '21 08:00 PM UTC. 01 weeks 06 days 15:43:06. Papersubmission and co
NEURIPS 2021 PAPER FAQ NeurIPS 2021 FAQ for Authors. We will update this page as new questions arise. Please check back regularly. If you do not find an answer to your question here, you are welcome to contact the NeurIPS 2021 program chairs at neurips2021pcs@gmail.com, but please make sure that you have read the call for papers and this document first..Submission process
NEURIPS 2021 CALL FOR PAPERS Call For Papers Abstract submission deadline: Wednesday, May 19, 2021 01:00 PM PDT Friday, May 21, 2021 01:00 PM PDT Full paper submission and co-author registration deadline: Wednesday, May 26, 2021 01:00 PM PDT Friday, May 28, 2021 01:00 PM PDT Supplementary material submission deadline: Wednesday, June 2, 2021 01:00 PM PDT Friday, June 4, 2021 01:00 PM PDT MONOTONE OPERATOR EQUILIBRIUM NETWORKS In this paper, we develop a new class of implicit-depth model based on the theory of monotone operators, the Monotone Operator Equilibrium Network (monDEQ). We show the close connection between finding the equilibrium point of an implicit network and solving a form of monotone operator splitting problem, which admits efficient solverswith
PREDRNN: RECURRENT NEURAL NETWORKS FOR PREDICTIVE LEARNING PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang School of Software Tsinghua University wangyb15@mails.tsinghua.edu.cn WHAT UNCERTAINTIES DO WE NEED IN BAYESIAN DEEP LEARNING 2Related Work Existing approaches to Bayesian deep learning capture either epistemic uncertainty alone, or aleatoric uncertainty alone . These uncertainties are formalised as probability distributionsover
DEPTH MAP PREDICTION FROM A SINGLE IMAGE USING A MULTI 9x9 conv Concatenate 2 stride 2x2 pool 11x11 conv 4 stride 2x2 pool Fine 1 Coarse 1 5x5 conv 2x2 pool Coarse 2 96 64 Coarse 5 256 256 Coarse 6 4096 63 384 META-WEIGHT-NET: LEARNING AN EXPLICIT MAPPING FOR SAMPLE Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu 1, Qi Xie , Lixuan Yi , Qian Zhao , Sanping Zhou 1, Zongben Xu , and Deyu Meng*2,1 1Xi’an Jiaotong University 2The Macau University of Science and Technology *Corresponding author:dymeng@mail.xjtu.edu.cn Abstract Current deep neural networks (DNNs) can easily overfit to biased training data with NON-LOCAL RECURRENT NETWORK FOR IMAGE RESTORATION Non-Local Recurrent Network for Image Restoration Ding Liu 1, Bihan Wen , Yuchen Fan , Chen Change Loy2, Thomas S. Huang1 1University of Illinois at Urbana-Champaign 2Nanyang Technological University {dingliu2, bwen3, yuchenf4, t-huang1}@illinois.edu ccloy@ntu.edu.sg Abstract Many classic methods have shown non-local self-similarity innatural images
DENOISED SMOOTHING: A PROVABLE DEFENSE FOR PRETRAINED Our approach applies to both the white-box and the black-box settings of the pretrained classifier. We refer to this defense as denoised smoothing, and we demonstrate its effectiveness through extensive experimentation on ImageNet and CIFAR-10. Finally, we use our approach to provably defend the Azure, Google, AWS, and ClarifAI image GRADIENT SURGERY FOR MULTI-TASK LEARNING Gradient Surgery for Multi-Task Learning Tianhe Yu 1, Saurabh Kumar , Abhishek Gupta2, Sergey Levine2, Karol Hausman3, Chelsea Finn1 Stanford University1, UC Berkeley2, Robotics at Google3 tianheyu@cs.stanford.edu Abstract While deep learning and deep INVESTIGATING GENDER BIAS IN LANGUAGE MODELS USING CAUSAL Investigating Gender Bias in Language Models Using Causal Mediation Analysis Jesse Vig 1 Sebastian Gehrmann *2Yonatan Belinkov Sharon Qian 2Daniel Nevo3 Yaron Singer Stuart Shieber2 1 Salesforce Research 2 Harvard University 3 Tel Aviv University jvig@salesforce.com danielnevo@tauex.tau.ac.il PIXEL-LEVEL CYCLE ASSOCIATION: A NEW PERSPECTIVE FOR Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation Guoliang Kang1, Yunchao Wei 2, Yi Yang , Yueting Zhuang3, Alexander G. Hauptmann1 1 School of Computer Science, Carnegie Mellon University 2 ReLER, University of Technology Sydney 3 Zhejiang University kgl.prml@gmail.com, alex@cs.cmu.edu {yunchao.wei,yi.yang}@uts.edu.au
CROSSTRANSFORMERS: SPATIALLY-AWARE FEW-SHOT TRANSFER CrossTransformers: spatially-aware few-shot transfer Carl Doersch Ankush Gupta Andrew Zissermany DeepMind, London yVGG, Department of Engineering Science, University of Oxford Abstract Given new tasks with very little data—such as new classes in a classification FALCON: FAST SPECTRAL INFERENCE ON ENCRYPTED DATA client. A non-interactive HE-enabled NN is a practical MLaaS solution with competitive accuracy for particular clients who have limited computing power and small network bandwidth. DEEP NETWORK FOR THE INTEGRATED 3D SENSING OF MULTIPLE Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images Andrei Zanfir, Elisabeta Marinoiu, Mihai Zanfir, Alin-Ionut Popa, and DEEP LEARNING FOR PRECIPITATION NOWCASTING: A BENCHMARK Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung Department of Computer Science and Engineering VARIATIONAL BAYESIAN MONTE CARLO Algorithm 1 Variational Bayesian Monte Carlo Input: target log joint f, starting point x 0, plausible bounds PLB , PUB , additional options 1: Initialization: t 0, initialize variational posterior ˚ 0, STOPSAMPLING false 2: repeat 3: t+1 4: if t, 1 then .Initial design, Section 3.5 5: Evaluate y 0 f (x 0)and add 0;y 0 to the training set 6: for 2:::n init do 7: Sample a new point x END-TO-END SYMMETRY PRESERVING INTER-ATOMIC POTENTIAL End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems Linfeng Zhang 1, Jiequn Han , Han Wang2 ;3, Wissam A. Saidi4;y, Roberto Car 1 ;5 6, Weinan E 7 8 z 1 Program in Applied and Computational Mathematics, Princeton University, USA 2 Institute of Applied Physics and Computational Mathematics, China 3 CAEP Software Center for High Performance2021 CONFERENCE
NeurIPS Board. Organizing Committee. Program Committee. NeurIPS Foundation. NeurIPS Thirty-fifth Annual Conference on Neural Information Processing Systems. NeurIPS 2021 is a Virtual-only Conference. Mon Dec 6th through Tue the 14th. (Monday is an industry expo) firstbacksecondback. NEURIPS 2021 PAPER FAQ NeurIPS 2021 FAQ for Authors. We will update this page as new questions arise. Please check back regularly. If you do not find an answer to your question here, you are welcome to contact the NeurIPS 2021 program chairs at neurips2021pcs@gmail.com, but please make sure that you have read the call for papers and this document first..Submission process
MONOTONE OPERATOR EQUILIBRIUM NETWORKS In this paper, we develop a new class of implicit-depth model based on the theory of monotone operators, the Monotone Operator Equilibrium Network (monDEQ). We show the close connection between finding the equilibrium point of an implicit network and solving a form of monotone operator splitting problem, which admits efficient solverswith
TEXTURE SYNTHESIS USING CONVOLUTIONAL NEURAL NETWORKS Texture Synthesis Using Convolutional Neural Networks Leon A. Gatys Centre for Integrative Neuroscience, University of Tubingen, Germany¨ Bernstein Center for Computational Neuroscience, Tubingen, Germany¨ PREDRNN: RECURRENT NEURAL NETWORKS FOR PREDICTIVE LEARNING PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang School of Software Tsinghua University wangyb15@mails.tsinghua.edu.cn COUNTERFACTUAL FAIRNESS In this paper, we develop a framework for modeling fairness using tools from causal inference. Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it the same in (a) the actual world and (b) a counterfactual world where the individual belonged to a different demographic group. We NON-LOCAL RECURRENT NETWORK FOR IMAGE RESTORATIONBURST IMAGE DENOISINGDCT IMAGE DENOISINGMEDICAL IMAGE DENOISINGRAW IMAGE DENOISING Non-Local Recurrent Network for Image Restoration Ding Liu 1, Bihan Wen , Yuchen Fan , Chen Change Loy2, Thomas S. Huang1 1University of Illinois at Urbana-Champaign 2Nanyang Technological University {dingliu2, bwen3, yuchenf4, t-huang1}@illinois.edu ccloy@ntu.edu.sg Abstract Many classic methods have shown non-local self-similarity innatural images
META-WEIGHT-NET: LEARNING AN EXPLICIT MAPPING FOR SAMPLE Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu 1, Qi Xie , Lixuan Yi , Qian Zhao , Sanping Zhou 1, Zongben Xu , and Deyu Meng*2,1 1Xi’an Jiaotong University 2The Macau University of Science and Technology *Corresponding author:dymeng@mail.xjtu.edu.cn Abstract Current deep neural networks (DNNs) can easily overfit to biased training data with THE RELEVANCE VECTOR MACHINE 654 ME. Tipping with 0 a vector of N + 1 hyperparameters.This introduction of an individual hy perparameter for every weight is the key feature of the model, and is ultimately ONLINE LEARNING FOR LATENT DIRICHLET ALLOCATIONLDA LATENT DIRICHLET ALLOCATIONSCIKIT LEARN LATENT DIRICHLET ALLOCATIONLATENT DIRICHLET ALLOCATION BLEILATENT DIRICHLET ALLOCATION EXAMPLELATENT DIRICHLETALLOCATION PAPER
Online Learning for Latent Dirichlet Allocation Matthew D. Hoffman Department of Computer Science Princeton University Princeton, NJ mdhoffma@cs.princeton.edu2021 CONFERENCE
NeurIPS Board. Organizing Committee. Program Committee. NeurIPS Foundation. NeurIPS Thirty-fifth Annual Conference on Neural Information Processing Systems. NeurIPS 2021 is a Virtual-only Conference. Mon Dec 6th through Tue the 14th. (Monday is an industry expo) firstbacksecondback. NEURIPS 2021 PAPER FAQ NeurIPS 2021 FAQ for Authors. We will update this page as new questions arise. Please check back regularly. If you do not find an answer to your question here, you are welcome to contact the NeurIPS 2021 program chairs at neurips2021pcs@gmail.com, but please make sure that you have read the call for papers and this document first..Submission process
MONOTONE OPERATOR EQUILIBRIUM NETWORKS In this paper, we develop a new class of implicit-depth model based on the theory of monotone operators, the Monotone Operator Equilibrium Network (monDEQ). We show the close connection between finding the equilibrium point of an implicit network and solving a form of monotone operator splitting problem, which admits efficient solverswith
TEXTURE SYNTHESIS USING CONVOLUTIONAL NEURAL NETWORKS Texture Synthesis Using Convolutional Neural Networks Leon A. Gatys Centre for Integrative Neuroscience, University of Tubingen, Germany¨ Bernstein Center for Computational Neuroscience, Tubingen, Germany¨ PREDRNN: RECURRENT NEURAL NETWORKS FOR PREDICTIVE LEARNING PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang School of Software Tsinghua University wangyb15@mails.tsinghua.edu.cn COUNTERFACTUAL FAIRNESS In this paper, we develop a framework for modeling fairness using tools from causal inference. Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it the same in (a) the actual world and (b) a counterfactual world where the individual belonged to a different demographic group. We NON-LOCAL RECURRENT NETWORK FOR IMAGE RESTORATIONBURST IMAGE DENOISINGDCT IMAGE DENOISINGMEDICAL IMAGE DENOISINGRAW IMAGE DENOISING Non-Local Recurrent Network for Image Restoration Ding Liu 1, Bihan Wen , Yuchen Fan , Chen Change Loy2, Thomas S. Huang1 1University of Illinois at Urbana-Champaign 2Nanyang Technological University {dingliu2, bwen3, yuchenf4, t-huang1}@illinois.edu ccloy@ntu.edu.sg Abstract Many classic methods have shown non-local self-similarity innatural images
META-WEIGHT-NET: LEARNING AN EXPLICIT MAPPING FOR SAMPLE Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu 1, Qi Xie , Lixuan Yi , Qian Zhao , Sanping Zhou 1, Zongben Xu , and Deyu Meng*2,1 1Xi’an Jiaotong University 2The Macau University of Science and Technology *Corresponding author:dymeng@mail.xjtu.edu.cn Abstract Current deep neural networks (DNNs) can easily overfit to biased training data with THE RELEVANCE VECTOR MACHINE 654 ME. Tipping with 0 a vector of N + 1 hyperparameters.This introduction of an individual hy perparameter for every weight is the key feature of the model, and is ultimately ONLINE LEARNING FOR LATENT DIRICHLET ALLOCATIONLDA LATENT DIRICHLET ALLOCATIONSCIKIT LEARN LATENT DIRICHLET ALLOCATIONLATENT DIRICHLET ALLOCATION BLEILATENT DIRICHLET ALLOCATION EXAMPLELATENT DIRICHLETALLOCATION PAPER
Online Learning for Latent Dirichlet Allocation Matthew D. Hoffman Department of Computer Science Princeton University Princeton, NJ mdhoffma@cs.princeton.edu SELF-PACED CONTRASTIVE LEARNING WITH HYBRID MEMORY FOR To solve these problems, we propose a novel self-paced contrastive learning framework with hybrid memory. The hybrid memory dynamically generates source-domain class-level, target-domain cluster-level and un-clustered instance-level supervisory signals for learning feature representations. Different from the conventional contrastive learning A UNIFIED APPROACH TO INTERPRETING MODEL PREDICTIONS A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Paul G. Allen School of Computer Science University of Washington Seattle, WA 98105 PIXEL-LEVEL CYCLE ASSOCIATION: A NEW PERSPECTIVE FOR Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation Guoliang Kang1, Yunchao Wei 2, Yi Yang , Yueting Zhuang3, Alexander G. Hauptmann1 1 School of Computer Science, Carnegie Mellon University 2 ReLER, University of Technology Sydney 3 Zhejiang University kgl.prml@gmail.com, alex@cs.cmu.edu {yunchao.wei,yi.yang}@uts.edu.au
INVESTIGATING GENDER BIAS IN LANGUAGE MODELS USING CAUSAL Investigating Gender Bias in Language Models Using Causal Mediation Analysis Jesse Vig 1 Sebastian Gehrmann *2Yonatan Belinkov Sharon Qian 2Daniel Nevo3 Yaron Singer Stuart Shieber2 1 Salesforce Research 2 Harvard University 3 Tel Aviv University jvig@salesforce.com danielnevo@tauex.tau.ac.il DEEP NETWORK FOR THE INTEGRATED 3D SENSING OF MULTIPLE Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images Andrei Zanfir, Elisabeta Marinoiu, Mihai Zanfir, Alin-Ionut Popa, and VARIATIONAL BAYESIAN MONTE CARLO Algorithm 1 Variational Bayesian Monte Carlo Input: target log joint f, starting point x 0, plausible bounds PLB , PUB , additional options 1: Initialization: t 0, initialize variational posterior ˚ 0, STOPSAMPLING false 2: repeat 3: t+1 4: if t, 1 then .Initial design, Section 3.5 5: Evaluate y 0 f (x 0)and add 0;y 0 to the training set 6: for 2:::n init do 7: Sample a new point x LEARNING STRUCTURED OUTPUT REPRESENTATION USING DEEP In this work, we develop a scalable deep conditional generative model for structured output variables using Gaussian latent variables. The model is trained efficiently in the framework of stochastic gradient variational Bayes, and allows a fast prediction using stochastic feed-forward inference. In addition, we provide novel strategies tobuild
DEPTH MAP PREDICTION FROM A SINGLE IMAGE USING A MULTI 9x9 conv Concatenate 2 stride 2x2 pool 11x11 conv 4 stride 2x2 pool Fine 1 Coarse 1 5x5 conv 2x2 pool Coarse 2 96 64 Coarse 5 256 256 Coarse 6 4096 63 384 DEEP LEARNING FOR PRECIPITATION NOWCASTING: A BENCHMARK Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung Department of Computer Science and Engineering DISTRIBUTED REPRESENTATIONS OF WORDS AND PHRASES AND THEIR training time. The basic Skip-gram formulation defines p(w t+j|w t)using the softmax function: p(w O|w I)= exp v′ w O ⊤v w I P W w=1 exp v′ ⊤v w I (2) where v wand v′ are the “input” and “output” vector representations of w, and W is the num- ber of words in the vocabulary. This formulation is impractical because thecost of computing
2021 CONFERENCE
General Chair. Marc'Aurelio Ranzato, Facebook AI Research. Program Chair. Alina Beygelzimer, Yahoo Research. Program Co-chairs. Percy Liang, Stanford University NEURIPS 2021 PAPER FAQ NeurIPS 2021 FAQ for Authors. We will update this page as new questions arise. Please check back regularly. If you do not find an answer to your question here, you are welcome to contact the NeurIPS 2021 program chairs at neurips2021pcs@gmail.com, but please make sure that you have read the call for papers and this document first..Submission process
MONOTONE OPERATOR EQUILIBRIUM NETWORKS Authors. Ezra Winston, J. Zico Kolter. Abstract. Implicit-depth models such as Deep Equilibrium Networks have recently been shown to match or exceed the performance of traditional deep networks while being much more memory efficient. NON-LOCAL RECURRENT NETWORK FOR IMAGE RESTORATIONBURST IMAGE DENOISINGDCT IMAGE DENOISINGMEDICAL IMAGE DENOISINGRAW IMAGE DENOISING Non-Local Recurrent Network for Image Restoration Ding Liu 1, Bihan Wen , Yuchen Fan , Chen Change Loy2, Thomas S. Huang1 1University of Illinois at Urbana-Champaign 2Nanyang Technological University {dingliu2, bwen3, yuchenf4, t-huang1}@illinois.edu ccloy@ntu.edu.sg Abstract Many classic methods have shown non-local self-similarity innatural images
TEXTURE SYNTHESIS USING CONVOLUTIONAL NEURAL NETWORKS Texture Synthesis Using Convolutional Neural Networks Leon A. Gatys Centre for Integrative Neuroscience, University of Tubingen, Germany¨ Bernstein Center for Computational Neuroscience, Tubingen, Germany¨ PREDRNN: RECURRENT NEURAL NETWORKS FOR PREDICTIVE LEARNING PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang School of Software Tsinghua University wangyb15@mails.tsinghua.edu.cn SEQUENCE TO SEQUENCE LEARNING WITH NEURAL NETWORKS Authors. Ilya Sutskever, Oriol Vinyals, Quoc V. Le. Abstract. Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. THE RELEVANCE VECTOR MACHINE 654 ME. Tipping with 0 a vector of N + 1 hyperparameters.This introduction of an individual hy perparameter for every weight is the key feature of the model, and is ultimately META-WEIGHT-NET: LEARNING AN EXPLICIT MAPPING FOR SAMPLE Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu 1, Qi Xie , Lixuan Yi , Qian Zhao , Sanping Zhou 1, Zongben Xu , and Deyu Meng*2,1 1Xi’an Jiaotong University 2The Macau University of Science and Technology *Corresponding author:dymeng@mail.xjtu.edu.cn Abstract Current deep neural networks (DNNs) can easily overfit to biased training data with ONLINE LEARNING FOR LATENT DIRICHLET ALLOCATIONLDA LATENT DIRICHLET ALLOCATIONSCIKIT LEARN LATENT DIRICHLET ALLOCATIONLATENT DIRICHLET ALLOCATION BLEILATENT DIRICHLET ALLOCATION EXAMPLELATENT DIRICHLETALLOCATION PAPER
Online Learning for Latent Dirichlet Allocation Matthew D. Hoffman Department of Computer Science Princeton University Princeton, NJ mdhoffma@cs.princeton.edu2021 CONFERENCE
General Chair. Marc'Aurelio Ranzato, Facebook AI Research. Program Chair. Alina Beygelzimer, Yahoo Research. Program Co-chairs. Percy Liang, Stanford University NEURIPS 2021 PAPER FAQ NeurIPS 2021 FAQ for Authors. We will update this page as new questions arise. Please check back regularly. If you do not find an answer to your question here, you are welcome to contact the NeurIPS 2021 program chairs at neurips2021pcs@gmail.com, but please make sure that you have read the call for papers and this document first..Submission process
MONOTONE OPERATOR EQUILIBRIUM NETWORKS Authors. Ezra Winston, J. Zico Kolter. Abstract. Implicit-depth models such as Deep Equilibrium Networks have recently been shown to match or exceed the performance of traditional deep networks while being much more memory efficient. NON-LOCAL RECURRENT NETWORK FOR IMAGE RESTORATIONBURST IMAGE DENOISINGDCT IMAGE DENOISINGMEDICAL IMAGE DENOISINGRAW IMAGE DENOISING Non-Local Recurrent Network for Image Restoration Ding Liu 1, Bihan Wen , Yuchen Fan , Chen Change Loy2, Thomas S. Huang1 1University of Illinois at Urbana-Champaign 2Nanyang Technological University {dingliu2, bwen3, yuchenf4, t-huang1}@illinois.edu ccloy@ntu.edu.sg Abstract Many classic methods have shown non-local self-similarity innatural images
TEXTURE SYNTHESIS USING CONVOLUTIONAL NEURAL NETWORKS Texture Synthesis Using Convolutional Neural Networks Leon A. Gatys Centre for Integrative Neuroscience, University of Tubingen, Germany¨ Bernstein Center for Computational Neuroscience, Tubingen, Germany¨ PREDRNN: RECURRENT NEURAL NETWORKS FOR PREDICTIVE LEARNING PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang School of Software Tsinghua University wangyb15@mails.tsinghua.edu.cn SEQUENCE TO SEQUENCE LEARNING WITH NEURAL NETWORKS Authors. Ilya Sutskever, Oriol Vinyals, Quoc V. Le. Abstract. Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. THE RELEVANCE VECTOR MACHINE 654 ME. Tipping with 0 a vector of N + 1 hyperparameters.This introduction of an individual hy perparameter for every weight is the key feature of the model, and is ultimately META-WEIGHT-NET: LEARNING AN EXPLICIT MAPPING FOR SAMPLE Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu 1, Qi Xie , Lixuan Yi , Qian Zhao , Sanping Zhou 1, Zongben Xu , and Deyu Meng*2,1 1Xi’an Jiaotong University 2The Macau University of Science and Technology *Corresponding author:dymeng@mail.xjtu.edu.cn Abstract Current deep neural networks (DNNs) can easily overfit to biased training data with ONLINE LEARNING FOR LATENT DIRICHLET ALLOCATIONLDA LATENT DIRICHLET ALLOCATIONSCIKIT LEARN LATENT DIRICHLET ALLOCATIONLATENT DIRICHLET ALLOCATION BLEILATENT DIRICHLET ALLOCATION EXAMPLELATENT DIRICHLETALLOCATION PAPER
Online Learning for Latent Dirichlet Allocation Matthew D. Hoffman Department of Computer Science Princeton University Princeton, NJ mdhoffma@cs.princeton.edu A UNIFIED APPROACH TO INTERPRETING MODEL PREDICTIONS A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Paul G. Allen School of Computer Science University of Washington Seattle, WA 98105 PIXEL-LEVEL CYCLE ASSOCIATION: A NEW PERSPECTIVE FOR Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation Guoliang Kang1, Yunchao Wei 2, Yi Yang , Yueting Zhuang3, Alexander G. Hauptmann1 1 School of Computer Science, Carnegie Mellon University 2 ReLER, University of Technology Sydney 3 Zhejiang University kgl.prml@gmail.com, alex@cs.cmu.edu {yunchao.wei,yi.yang}@uts.edu.au
DENOISED SMOOTHING: A PROVABLE DEFENSE FOR PRETRAINED Authors. Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor, J. Zico Kolter. Abstract. We present a method for provably defending any pretrained image classifier against $\ell_p$ adversarial attacks. COUNTERFACTUAL FAIRNESS Authors. Matt J. Kusner, Joshua Loftus, Chris Russell, Ricardo Silva. Abstract. Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. INVESTIGATING GENDER BIAS IN LANGUAGE MODELS USING CAUSAL Investigating Gender Bias in Language Models Using Causal Mediation Analysis Jesse Vig 1 Sebastian Gehrmann *2Yonatan Belinkov Sharon Qian 2Daniel Nevo3 Yaron Singer Stuart Shieber2 1 Salesforce Research 2 Harvard University 3 Tel Aviv University jvig@salesforce.com danielnevo@tauex.tau.ac.il IMAGENET CLASSIFICATION WITH DEEP CONVOLUTIONAL NEURAL ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky University of Toronto kriz@cs.utoronto.ca Ilya Sutskever University of Toronto TOWARDS ACCURATE BINARY CONVOLUTIONAL NEURAL NETWORK Towards Accurate Binary Convolutional Neural Network Xiaofan Lin Cong Zhao Wei Pan* DJI Innovations Inc, Shenzhen, China {xiaofan.lin, cong.zhao, wei.pan}@dji.com VARIATIONAL BAYESIAN MONTE CARLO Algorithm 1 Variational Bayesian Monte Carlo Input: target log joint f, starting point x 0, plausible bounds PLB , PUB , additional options 1: Initialization: t 0, initialize variational posterior ˚ 0, STOPSAMPLING false 2: repeat 3: t+1 4: if t, 1 then .Initial design, Section 3.5 5: Evaluate y 0 f (x 0)and add 0;y 0 to the training set 6: for 2:::n init do 7: Sample a new point x MULTIPLE FUTURES PREDICTION (a) Graphical model of the MFP. Solid nodes denote observed. Cross agent interaction edges are shaded for clarity. x tdenotes both the state and contextual information from timesteps 1 to t. A^2-NETS: DOUBLE ATTENTION NETWORKS A2-Nets: Double Attention Networks Yunpeng Chen National University of Singapore chenyunpeng@u.nus.edu Yannis Kalantidis Facebook Research yannisk@fb.com Jianshu Li National University of SingaporeToggle navigation
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NEURIPS | 2021
Thirty-fifth Conference on Neural Information Processing SystemsYear (2021)
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* NeurIPS Foundation NeurIPS Thirty-fifth Annual Conference on Neural InformationProcessing Systems
NeurIPS 2021 is a Virtual-only Conference Mon Dec 6th through Tue the 14th (Monday is an industry expo)firstbacksecondback
ANNOUNCEMENTS
More information about the schedule and virtual conference will become available in this blog post .IMPORTANT DATES
Conference Sessions, Tutorials, Workshops and Expo Mon Dec 6th through Tue the 14th Abstract Submission Deadline May 21 '21 08:00 PM UTC * 00 weeks 00 days 00:00:00 Applications for Workshops Open May 28 '21 04:00 PM UTC * 00 weeks 00 days 00:00:00 Paper submission and co-author registration deadline May 28 '21 08:00 PM UTC * 00 weeks 00 days 00:00:00 Datasets and Benchmarks Submission deadline (1st round) Jun 07 '21(Anywhere on Earth) Workshop Application Deadline Jun 19 '21 01:00 AM UTC * 02 weeks 00 days 11:26:13 Meetup Submission Deadline Jul 01 '21(Anywhere on Earth) 03 weeks 06 days 22:26:12 Workshop Notifications Jul 19 '21 01:00 AM UTC * 06 weeks 02 days 11:26:13 Meetup Notification of Acceptance Aug 03 '21 01:00 AM UTC * Datasets and Benchmarks Submission deadline (2nd round) Aug 27 '21(Anywhere on Earth)Author Notification
Sep 28 '21 08:00 PM UTC * Paper Submission Camera Ready Deadline Oct 26 '21 08:00 PM UTC *All dates »
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2021 ORGANIZING COMMITTEEGENERAL CHAIR
Marc'Aurelio Ranzato, Facebook AI ResearchPROGRAM CHAIR
Alina Beygelzimer, Yahoo ResearchPROGRAM CO-CHAIRS
Percy Liang, Stanford University Jenn Wortman Vaughan, Microsoft Research Yann Dauphin, Google BrainWORKSHOP CHAIRS
Ndapa Nakashole, University of California, San Diego Anna Goldenberg, SickKids Research Institute, University of Toronto Sanmi Koyejo, Uni of Illinois at UC & Google Research Tristan Naumann, Microsoft ResearchTUTORIAL CHAIRS
Marc Deisenroth, University College, London Meire Fortunato, DeepMind DEMONSTRATION AND COMPETITION CHAIRS Douwe Kiela, Facebook AI Research Barbara Caputo, Politecnico di TorinoEXPO CHAIR
Pablo Samuel Castro, Google Research Yale Song, Microsoft Research Ivor Tsang, University of Technology, SydneyMEETUP CHAIRS
Emtiyaz Khan, RIKEN Louvere Walker-Hannon, MathWorks Olivia Muza, STEAM Women Jacqueline Forien, Machinelearning.Fr. Ryuichiro Hataya, University of Tokyo Rodrigo Beceiro, Marvik AI COMMUNICATION CHAIRS Shakir Mohamed, DeepMind Emily Denton, Google ResearchSPONSORSHIP CHAIRS
Simon Lacoste-Julien, University of Montreal Tie-Yan Liu, Microsoft Research DIVERSITY, INCLUSION & ACCESSIBILITY CHAIRS Lester Mackey, Microsoft Research & Stanford Maria Skoularidou, University of Cambridge Pascale Fung, Hong Kong Uni. of Science and TechnologySOCIAL CHAIRS
Freddie Kalaitzis, University of Oxford / FDL Gautam Kamath, University of Waterloo DATA & BENCHMARK CHAIRS Joaquin Vanschoren, Eindhoven Uni. of Technology,OpenML
Serena Yeung, Stanford University ONLINE EXPERIENCE CHAIRS Y-Lan Boureau, Facebook AI Research Hendrik Strobelt, MIT-IBM Watson AI Lab ETHICS REVIEW CHAIRSSamy Bengio, Apple
Inioluwa Deborah Raji, Mozilla FoundationWORKFLOW MANAGER
Zhenyu (Sherry) Xue
EXECUTIVE DIRECTOR
Mary Ellen Perry, Level 5 EventsIT DIRECTOR
Lee Campbell, Level 5 Events ------------------------- We would also like to thank TPMSand
OpenReview for their service which enabled better assignment of papers to reviewers, and AMiner for helping us mine co-authorship information. NEURAL INFORMATION PROCESSING SYSTEMS FOUNDATION BOARD 2021PRESIDENT
Terrence Sejnowski, The Salk InstituteTREASURER
Marian Stewart Bartlett, Apple Inc.SECRETARY
Michael Mozer, Google ResearchBOARD MEMBERS
Samy Bengio, Apple
Corinna Cortes, Google Research Isabelle Guyon, U. Paris-Saclay & ChaLearn Hugo Larochelle, Google Research Neil D. Lawrence, Cambridge University Daniel D. Lee, University of Pennsylvania Marc'Aurelio Ranzato, Facebook Masashi Sugiyama, RIKEN & The University of Tokyo Hanna Wallach, Microsoft ResearchLEGAL ADVISOR
David Kirkpatrick
EXECUTIVE DIRECTOR
Mary Ellen Perry, Level 5 EventsEMERITUS MEMBERS
Gary Blasdel, Harvard Medical School T. L. Fine, Cornell University Eve Marder, Brandeis UniversityADVISORY BOARD
Peter Bartlett, Queensland University and University California,Berkeley
Sue Becker, McMaster University, Ontario, Canada Yoshua Bengio, University of Montreal, Canada Léon Bottou, Facebook AI Research and NYU Chris J.C. Burges, Microsoft Research Jack Cowan, University of Chicago Thomas G. Dietterich, Oregon State University Zoubin Ghahramani, University of Cambridge Stephen Hanson, Rutgers University Michael I. Jordan, University of California, Berkeley Michael Kearns, University of Pennsylvania Scott Kirkpatrick, Hebrew University, Jerusalem Daphne Koller, Stanford University John Lafferty, Yale UniversityTodd K. Leen
Richard Lippmann, Massachusetts Institute of Technology Ulrike von Luxburg, University of Tübingen Bartlett Mel, University of Southern CaliforniaJohn Moody, JEM
John C. Platt, Google Fernando Pereira, Google Research Gerald Tesauro, IBM Watson Labs Sebastian Thrun, Stanford University Dave Touretzky, Carnegie Mellon University Lawrence Saul, University of California, San Diego Bernhard Schölkopf, Max Planck Institute for Intelligent Systems,Tübingen/Stuttgart
Dale Schuurmans, University of Alberta, Canada John Shawe-Taylor, University College London Yoram Singer, Princeton University Sara A. Solla, Northwestern University Medical School Yair Weiss, Hebrew University of Jerusalem Max Welling, University of Amsterdam Chris Williams, University of Edinburgh Rich Zemel, University of TorontoABOUT NEURIPS
The purpose of the Neural Information Processing Systems annual meeting is to foster the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects. The core focus is peer-reviewed novel research which is presented and discussed in the general session, along with invited talks by leaders in their field. On Sunday is an Expo, where our top industry sponsors give talks, panels, demos, and workshops on topics that are of academic interest. On Monday are tutorials, which cover a broad background on current lines of inquiry, affinity group meetings, and the opening talk & reception. The general sessions are held Tuesday - Thursday, and include talks, posters, and demonstrations. Friday - Saturday are the workshops, which are smaller meetings focused on current topics, and provide an informal, cutting edge venue for discussion. More about the foundation » Successful Page Load Do not remove: This comment is monitored to verify that the site isworking properly
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