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ICLR | 2021
About Us. The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of 2022 DATES AND DEADLINES ICLR 2022 Meeting Dates. The Tenth annual conference is held Mon Apr 25th through Fri the 29th, 2022 at Virtual. Session. Start Date. Conference Sessions. Mon Apr 25th through Fri the 29th. Timezone: ».PRICING - ICLR
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ICLR: NEVER GIVE UP: LEARNING DIRECTED EXPLORATION STRATEGIES Abstract: We propose a reinforcement learning agent to solve hard exploration games by learning a range of directed exploratory policies. We construct an episodic memory-based intrinsic reward using k-nearest neighbors over the agent's recent experience to train the directed exploratory policies, thereby encouraging the agent to repeatedly revisit all states in its environment. ICLR: ENCODING WORD ORDER IN COMPLEX EMBEDDINGS We extend CNN, RNN and Transformer NNs to complex-valued versions to incorporate our complex embedding (we make all code available). Experiments on text classification, machine translation and language modeling show gains over both classical word embeddings and position-enriched word embeddings. To our knowledge, this is the firstwork in NLP
ICLR: BAYESOPT ADVERSARIAL ATTACK Abstract: Black-box adversarial attacks require a large number of attempts before finding successful adversarial examples that are visually indistinguishable from the original input. Current approaches relying on substitute model training, gradient estimation or genetic algorithms often require an excessive number of queries. ICLR: YOU ONLY TRAIN ONCE: LOSS-CONDITIONAL TRAINING OF Abstract: In many machine learning problems, loss functions are weighted sums of several terms. A typical approach to dealing with these is to train multiple separate models with different selections of weights and then either choose the best one according to some criterion or keep multiple models if it is desirable to maintain a diverse set of solutions. ICLR: PHYSICS-AWARE DIFFERENCE GRAPH NETWORKS FOR SPARSELY In this paper, we propose a novel architecture, Physics-aware Difference Graph Networks (PA-DGN), which exploits neighboring information to learn finite differences inspired by physics equations. PA-DGN leverages data-driven end-to-end learning to discover underlying dynamical relations between the spatial and temporal differences in given REGULARIZED INVERSE REINFORCEMENT LEARNING Regularized Inverse Reinforcement Learning Wonseok Jeon 1;2, Chen-Yang Su , Paul Barde , Thang Doan1;2, Derek Nowrouzezahrai 1 ;2, Joelle Pineau 3 1Mila - Quebec AI Institute 2McGill University 3Facebook AI Research ICLR 2021 Wonseok Jeon Regularized Inverse ReinforcementLearning ICLR
ICLR - 2022 CONFERENCEFAQABOUT ICLRVIRTUAL CONFERENCE AUTHOR INSTRUCTIONSCALL FOR PAPERSRESET PASSWORDSPONSOR INFO About Us. The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields ofICLR | 2021
About Us. The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of 2022 DATES AND DEADLINES ICLR 2022 Meeting Dates. The Tenth annual conference is held Mon Apr 25th through Fri the 29th, 2022 at Virtual. Session. Start Date. Conference Sessions. Mon Apr 25th through Fri the 29th. Timezone: ».PRICING - ICLR
Do not remove: This comment is monitored to verify that the site isworking properly
ICLR: NEVER GIVE UP: LEARNING DIRECTED EXPLORATION STRATEGIES Abstract: We propose a reinforcement learning agent to solve hard exploration games by learning a range of directed exploratory policies. We construct an episodic memory-based intrinsic reward using k-nearest neighbors over the agent's recent experience to train the directed exploratory policies, thereby encouraging the agent to repeatedly revisit all states in its environment. ICLR: ENCODING WORD ORDER IN COMPLEX EMBEDDINGS We extend CNN, RNN and Transformer NNs to complex-valued versions to incorporate our complex embedding (we make all code available). Experiments on text classification, machine translation and language modeling show gains over both classical word embeddings and position-enriched word embeddings. To our knowledge, this is the firstwork in NLP
ICLR: BAYESOPT ADVERSARIAL ATTACK Abstract: Black-box adversarial attacks require a large number of attempts before finding successful adversarial examples that are visually indistinguishable from the original input. Current approaches relying on substitute model training, gradient estimation or genetic algorithms often require an excessive number of queries. ICLR: YOU ONLY TRAIN ONCE: LOSS-CONDITIONAL TRAINING OF Abstract: In many machine learning problems, loss functions are weighted sums of several terms. A typical approach to dealing with these is to train multiple separate models with different selections of weights and then either choose the best one according to some criterion or keep multiple models if it is desirable to maintain a diverse set of solutions. ICLR: PHYSICS-AWARE DIFFERENCE GRAPH NETWORKS FOR SPARSELY In this paper, we propose a novel architecture, Physics-aware Difference Graph Networks (PA-DGN), which exploits neighboring information to learn finite differences inspired by physics equations. PA-DGN leverages data-driven end-to-end learning to discover underlying dynamical relations between the spatial and temporal differences in given REGULARIZED INVERSE REINFORCEMENT LEARNING Regularized Inverse Reinforcement Learning Wonseok Jeon 1;2, Chen-Yang Su , Paul Barde , Thang Doan1;2, Derek Nowrouzezahrai 1 ;2, Joelle Pineau 3 1Mila - Quebec AI Institute 2McGill University 3Facebook AI Research ICLR 2021 Wonseok Jeon Regularized Inverse ReinforcementLearning ICLR
2020 CONFERENCE
About Us. The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields ofICLR 2020
ICLR 2020. Showing papers for . ×. title keyword author. list compact detail. B-Spline CNNs on Lie groups. Erik J Bekkers. Depth-Adaptive Transformer. Maha Elbayad, Jiatao Gu, Edouard Grave, Michael Auli.ICLR 2021 SCHEDULE
Colin Raffel · Adam Roberts · Amanda Askell · Daphne Ippolito · Ethan Dyer · Guy Gur-Ari · Jared Kaplan · Jascha Sohl-Dickstein · Katherine Lee · Melanie Subbiah · Sam McCandlish · Tom Brown · William Fedus · Vedant Misra · Ambrose Slone · Daniel Freeman.Workshop.
ICLR 2020 AUTHOR GUIDE The deadline for submitting camera-ready submission is the 14 February 2020. We strongly recommend you complete this in early January to avoid conflicts with the submissions deadlines for ICML2020. Some guidance on preparing your camera-ready version. Insert the ICLR: BAYESOPT ADVERSARIAL ATTACK Abstract: Black-box adversarial attacks require a large number of attempts before finding successful adversarial examples that are visually indistinguishable from the original input. Current approaches relying on substitute model training, gradient estimation or genetic algorithms often require an excessive number of queries. ICLR: MEASURING AND IMPROVING THE USE OF GRAPH INFORMATION Abstract: Graph neural networks (GNNs) have been widely used for representation learning on graph data. However, there is limited understanding on how much performance GNNs actually gain from graph data. This paper introduces a context-surrounding GNN framework and proposes two smoothness metrics to measure the quantity and quality of information obtained from graph data. ICLR: NEURAL TEXT GENERATION WITH UNLIKELIHOOD TRAINING Abstract: Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core. In particular, standard likelihood training and decoding leads to dull and repetitive outputs. While some post-hoc fixes have been proposed, in particular top-k and nucleus sampling, they do not address the fact that the token-level probabilities predicted ICLR: FEDERATED ADVERSARIAL DOMAIN ADAPTATION Abstract: Federated learning improves data privacy and efficiency in machine learning performed over networks of distributed devices, such as mobile phones, IoT and wearable devices, etc. Yet models trained with federated learning can still fail to generalize to new devices due to the problem of domain shift. Domain shift occurs when the labeled data collected by source nodes statistically ICLR: A CRITICAL ANALYSIS OF SELF-SUPERVISION, OR WHAT WE Abstract: We look critically at popular self-supervision techniques for learning deep convolutional neural networks without manual labels. We show that three different and representative methods, BiGAN, RotNet and DeepCluster, can learn the first few layers of a convolutional network from a single image as well as using millions of images and manual labels, provided that strong data WORKSHOP ON CAUSAL LEARNING FOR DECISION MAKING Description: Machine learning has enabled significant improvements in many areas. Most of these ML methods are based on inferring statistical correlations, they can become unreliable where spurious correlations present in the training data do not hold in the testing setting. One way of tackling this problem is to learn the causalstructure of
ICLR - 2022 CONFERENCEFAQABOUT ICLRVIRTUAL CONFERENCE AUTHOR INSTRUCTIONSCALL FOR PAPERSRESET PASSWORDSPONSOR INFO About Us. The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields ofICLR | 2021
About Us. The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of 2022 DATES AND DEADLINES ICLR 2022 Meeting Dates. The Tenth annual conference is held Mon Apr 25th through Fri the 29th, 2022 at Virtual. Session. Start Date. Conference Sessions. Mon Apr 25th through Fri the 29th. Timezone: ». PRICING - ICLRICLR 2021 CFPICLR 2021 DEADLINEICLR 2021 OPEN REVIEWICLR 2021 PAPER SUBMISSIONICLR CONFERENCEICLR DEADLINE Do not remove: This comment is monitored to verify that the site isworking properly
ICLR: NEVER GIVE UP: LEARNING DIRECTED EXPLORATION STRATEGIESREINFORCEMENT LEARNING EXPLORATIONEXPLORATION EXPLOITATIONSTRATEGIC VS DEEP LEARNINGEXPLORATION REINFORCEMENT LEARNINGREINFORCEMENT LEARNING TRAJECTORYSELF DIRECTED LEARNINGSTRATEGIES
Abstract: We propose a reinforcement learning agent to solve hard exploration games by learning a range of directed exploratory policies. We construct an episodic memory-based intrinsic reward using k-nearest neighbors over the agent's recent experience to train the directed exploratory policies, thereby encouraging the agent to repeatedly revisit all states in its environment. ICLR: ENCODING WORD ORDER IN COMPLEX EMBEDDINGS We extend CNN, RNN and Transformer NNs to complex-valued versions to incorporate our complex embedding (we make all code available). Experiments on text classification, machine translation and language modeling show gains over both classical word embeddings and position-enriched word embeddings. To our knowledge, this is the firstwork in NLP
ICLR: BAYESOPT ADVERSARIAL ATTACK Abstract: Black-box adversarial attacks require a large number of attempts before finding successful adversarial examples that are visually indistinguishable from the original input. Current approaches relying on substitute model training, gradient estimation or genetic algorithms often require an excessive number of queries. ICLR: YOU ONLY TRAIN ONCE: LOSS-CONDITIONAL TRAINING OF Abstract: In many machine learning problems, loss functions are weighted sums of several terms. A typical approach to dealing with these is to train multiple separate models with different selections of weights and then either choose the best one according to some criterion or keep multiple models if it is desirable to maintain a diverse set of solutions. ICLR: PHYSICS-AWARE DIFFERENCE GRAPH NETWORKS FOR SPARSELY In this paper, we propose a novel architecture, Physics-aware Difference Graph Networks (PA-DGN), which exploits neighboring information to learn finite differences inspired by physics equations. PA-DGN leverages data-driven end-to-end learning to discover underlying dynamical relations between the spatial and temporal differences in given REGULARIZED INVERSE REINFORCEMENT LEARNING Regularized Inverse Reinforcement Learning Wonseok Jeon 1;2, Chen-Yang Su , Paul Barde , Thang Doan1;2, Derek Nowrouzezahrai 1 ;2, Joelle Pineau 3 1Mila - Quebec AI Institute 2McGill University 3Facebook AI Research ICLR 2021 Wonseok Jeon Regularized Inverse ReinforcementLearning ICLR
ICLR - 2022 CONFERENCEFAQABOUT ICLRVIRTUAL CONFERENCE AUTHOR INSTRUCTIONSCALL FOR PAPERSRESET PASSWORDSPONSOR INFO About Us. The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields ofICLR | 2021
About Us. The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of 2022 DATES AND DEADLINES ICLR 2022 Meeting Dates. The Tenth annual conference is held Mon Apr 25th through Fri the 29th, 2022 at Virtual. Session. Start Date. Conference Sessions. Mon Apr 25th through Fri the 29th. Timezone: ». PRICING - ICLRICLR 2021 CFPICLR 2021 DEADLINEICLR 2021 OPEN REVIEWICLR 2021 PAPER SUBMISSIONICLR CONFERENCEICLR DEADLINE Do not remove: This comment is monitored to verify that the site isworking properly
ICLR: NEVER GIVE UP: LEARNING DIRECTED EXPLORATION STRATEGIESREINFORCEMENT LEARNING EXPLORATIONEXPLORATION EXPLOITATIONSTRATEGIC VS DEEP LEARNINGEXPLORATION REINFORCEMENT LEARNINGREINFORCEMENT LEARNING TRAJECTORYSELF DIRECTED LEARNINGSTRATEGIES
Abstract: We propose a reinforcement learning agent to solve hard exploration games by learning a range of directed exploratory policies. We construct an episodic memory-based intrinsic reward using k-nearest neighbors over the agent's recent experience to train the directed exploratory policies, thereby encouraging the agent to repeatedly revisit all states in its environment. ICLR: ENCODING WORD ORDER IN COMPLEX EMBEDDINGS We extend CNN, RNN and Transformer NNs to complex-valued versions to incorporate our complex embedding (we make all code available). Experiments on text classification, machine translation and language modeling show gains over both classical word embeddings and position-enriched word embeddings. To our knowledge, this is the firstwork in NLP
ICLR: BAYESOPT ADVERSARIAL ATTACK Abstract: Black-box adversarial attacks require a large number of attempts before finding successful adversarial examples that are visually indistinguishable from the original input. Current approaches relying on substitute model training, gradient estimation or genetic algorithms often require an excessive number of queries. ICLR: YOU ONLY TRAIN ONCE: LOSS-CONDITIONAL TRAINING OF Abstract: In many machine learning problems, loss functions are weighted sums of several terms. A typical approach to dealing with these is to train multiple separate models with different selections of weights and then either choose the best one according to some criterion or keep multiple models if it is desirable to maintain a diverse set of solutions. ICLR: PHYSICS-AWARE DIFFERENCE GRAPH NETWORKS FOR SPARSELY In this paper, we propose a novel architecture, Physics-aware Difference Graph Networks (PA-DGN), which exploits neighboring information to learn finite differences inspired by physics equations. PA-DGN leverages data-driven end-to-end learning to discover underlying dynamical relations between the spatial and temporal differences in given REGULARIZED INVERSE REINFORCEMENT LEARNING Regularized Inverse Reinforcement Learning Wonseok Jeon 1;2, Chen-Yang Su , Paul Barde , Thang Doan1;2, Derek Nowrouzezahrai 1 ;2, Joelle Pineau 3 1Mila - Quebec AI Institute 2McGill University 3Facebook AI Research ICLR 2021 Wonseok Jeon Regularized Inverse ReinforcementLearning ICLR
2020 CONFERENCE
About Us. The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields ofICLR 2020
ICLR 2020. Showing papers for . ×. title keyword author. list compact detail. B-Spline CNNs on Lie groups. Erik J Bekkers. Depth-Adaptive Transformer. Maha Elbayad, Jiatao Gu, Edouard Grave, Michael Auli.ICLR 2021 SCHEDULE
Colin Raffel · Adam Roberts · Amanda Askell · Daphne Ippolito · Ethan Dyer · Guy Gur-Ari · Jared Kaplan · Jascha Sohl-Dickstein · Katherine Lee · Melanie Subbiah · Sam McCandlish · Tom Brown · William Fedus · Vedant Misra · Ambrose Slone · Daniel Freeman.Workshop.
ICLR 2020 AUTHOR GUIDE The deadline for submitting camera-ready submission is the 14 February 2020. We strongly recommend you complete this in early January to avoid conflicts with the submissions deadlines for ICML2020. Some guidance on preparing your camera-ready version. Insert the ICLR: BAYESOPT ADVERSARIAL ATTACK Abstract: Black-box adversarial attacks require a large number of attempts before finding successful adversarial examples that are visually indistinguishable from the original input. Current approaches relying on substitute model training, gradient estimation or genetic algorithms often require an excessive number of queries. ICLR: MEASURING AND IMPROVING THE USE OF GRAPH INFORMATION Abstract: Graph neural networks (GNNs) have been widely used for representation learning on graph data. However, there is limited understanding on how much performance GNNs actually gain from graph data. This paper introduces a context-surrounding GNN framework and proposes two smoothness metrics to measure the quantity and quality of information obtained from graph data. ICLR: NEURAL TEXT GENERATION WITH UNLIKELIHOOD TRAINING Abstract: Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core. In particular, standard likelihood training and decoding leads to dull and repetitive outputs. While some post-hoc fixes have been proposed, in particular top-k and nucleus sampling, they do not address the fact that the token-level probabilities predicted ICLR: FEDERATED ADVERSARIAL DOMAIN ADAPTATION Abstract: Federated learning improves data privacy and efficiency in machine learning performed over networks of distributed devices, such as mobile phones, IoT and wearable devices, etc. Yet models trained with federated learning can still fail to generalize to new devices due to the problem of domain shift. Domain shift occurs when the labeled data collected by source nodes statistically ICLR: A CRITICAL ANALYSIS OF SELF-SUPERVISION, OR WHAT WE Abstract: We look critically at popular self-supervision techniques for learning deep convolutional neural networks without manual labels. We show that three different and representative methods, BiGAN, RotNet and DeepCluster, can learn the first few layers of a convolutional network from a single image as well as using millions of images and manual labels, provided that strong data WORKSHOP ON CAUSAL LEARNING FOR DECISION MAKING Description: Machine learning has enabled significant improvements in many areas. Most of these ML methods are based on inferring statistical correlations, they can become unreliable where spurious correlations present in the training data do not hold in the testing setting. One way of tackling this problem is to learn the causalstructure of
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ICLR @ Formerly Addis Ababa ·The Eighth International Conference on Learning Representations Virtual Conference, Formerly Addis Ababa ETHIOPIA Sun Apr 26th through May 1stVIRTUAL CONFERENCE
ICLR 2020 Virtual Site » -------------------------GENERAL CHAIR
* Alexander Rush, Cornell Tech SENIOR PROGRAM CHAIR * Shakir Mohamed, DeepMindPROGRAM CHAIRS
* Dawn Song, UC Berkeley * Kyunghyun Cho, NYU & FAIR * Martha White, University of AlbertaAREA CHAIRS
* Area Chairs »
-------------------------WORKSHOP CHAIRS
* Gabriel Synnaeve, Facebook AI Research * Asja Fischer, Ruhr University Bochum DIVERSITY+INCLUSION CHAIRS * Animashree Anandkumar - Cal Tech / NVidia * Kevin Swersky - Google AILOGISTICS CHAIRS
* Timnit Gebru - Google Brain * Esube Bekele - In-Q-TelSOCIALS CHAIR
* Adam White - DeepMindCONTACT
The organizers can be contacted here.
SPONSORS
The generous support of our sponsors allowed us to reduce our ticket price by about 50%, and support diverisy at the meeting with travel awards. In addition, many accepted papers at the conference were contributed by our sponors. View ICLR 2020 sponsors » Become a 2020 Sponsor »ABOUT US
THE INTERNATIONAL CONFERENCE ON LEARNING REPRESENTATIONS (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing CUTTING-EDGE RESEARCH ON ALL ASPECTS OF DEEP LEARNING USED IN THE FIELDS OF ARTIFICIAL INTELLIGENCE, STATISTICS AND DATA SCIENCE, AS WELL AS IMPORTANT APPLICATION AREAS SUCH AS MACHINE VISION, COMPUTATIONAL BIOLOGY, SPEECH RECOGNITION, TEXT UNDERSTANDING, GAMING, AND ROBOTICS. Participants at ICLR span a wide range of backgrounds, FROM ACADEMIC AND INDUSTRIAL RESEARCHERS, TO ENTREPRENEURS AND ENGINEERS, TO GRADUATE STUDENTS AND POSTDOCS. A non-exhaustive list of relevant topics explored at the conferenceinclude:
* unsupervised, semi-supervised, and supervised representationlearning
* representation learning for planning and reinforcement learning * metric learning and kernel learning * sparse coding and dimensionality expansion * hierarchical models * optimization for representation learning * learning representations of outputs or states * implementation issues, parallelization, software platforms,hardware
* applications in vision, audio, speech, natural language processing, robotics, neuroscience, or any other field Successful Page Load Do not remove: This comment is monitored to verify that the site isworking properly
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