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π GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
π₯ Zhilin Yang, Jake, Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann LeCun
π PDF
π Self-Imitation Learning
π₯ Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee
π PDF
π Learning in POMDPs with Monte Carlo Tree Search
π₯ Sammie Katt, Frans A. Oliehoek, Christopher Amato
π PDF
π Stochastic Variance-Reduced Policy Gradient
π₯ Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli
π PDF
π Improving Consistency-Based Semi-Supervised Learning with Weight Averaging
π₯ Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson
π PDF
π Autoregressive Quantile Networks for Generative Modeling
π₯ Georg Ostrovski, Will Dabney, RΓ©mi Munos
π PDF
π Improved Density-Based Spatio--Textual Clustering on Social Media
π₯ Minh D. Nguyen, Won-Yong Shin
π PDF
π The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing
π₯ Cencheng Shen, Joshua T. Vogelstein
π PDF
π NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical Usage
π₯ Alexander Wong
π PDF
π Neural Stethoscopes: Unifying Analytic, Auxiliary and Adversarial Network Probing
π₯ Fabian B. Fuchs, Oliver Groth, Adam R. Kosoriek, Alex Bewley, Markus Wulfmeier, Andrea Vedaldi, Ingmar Posner
π PDF
π Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
π₯ Marton Havasi, JosΓ© Miguel HernΓ‘ndez Lobato, Juan JosΓ© Murillo Fuentes
π PDF
π Low-rank geometric mean metric learning
π₯ Mukul Bhutani, Pratik Jawanpuria, Hiroyuki Kasai, Bamdev Mishra
π PDF
π The committee machine: Computational to statistical gaps in learning a two-layers neural network
π₯ Benjamin Aubin, Antoine Maillard, Jean Barbier, Florent Krzakala, Nicolas Macris, Lenka ZdeborovΓ‘
π PDF
π Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors
π₯ Atsushi Nitanda, Taiji Suzuki
π PDF
π ServeNet: A Deep Neural Network for Web Service Classification
π₯ Yilong Yang, Peng Liu, Lianchao Ding, Bingqing Shen, Weiru Wang
π PDF
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π GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
π₯ Zhilin Yang, Jake, Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann LeCun
π PDF
π Self-Imitation Learning
π₯ Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee
π PDF
π Learning in POMDPs with Monte Carlo Tree Search
π₯ Sammie Katt, Frans A. Oliehoek, Christopher Amato
π PDF
π Stochastic Variance-Reduced Policy Gradient
π₯ Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli
π PDF
π Improving Consistency-Based Semi-Supervised Learning with Weight Averaging
π₯ Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson
π PDF
π Autoregressive Quantile Networks for Generative Modeling
π₯ Georg Ostrovski, Will Dabney, RΓ©mi Munos
π PDF
π Improved Density-Based Spatio--Textual Clustering on Social Media
π₯ Minh D. Nguyen, Won-Yong Shin
π PDF
π The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing
π₯ Cencheng Shen, Joshua T. Vogelstein
π PDF
π NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical Usage
π₯ Alexander Wong
π PDF
π Neural Stethoscopes: Unifying Analytic, Auxiliary and Adversarial Network Probing
π₯ Fabian B. Fuchs, Oliver Groth, Adam R. Kosoriek, Alex Bewley, Markus Wulfmeier, Andrea Vedaldi, Ingmar Posner
π PDF
π Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
π₯ Marton Havasi, JosΓ© Miguel HernΓ‘ndez Lobato, Juan JosΓ© Murillo Fuentes
π PDF
π Low-rank geometric mean metric learning
π₯ Mukul Bhutani, Pratik Jawanpuria, Hiroyuki Kasai, Bamdev Mishra
π PDF
π The committee machine: Computational to statistical gaps in learning a two-layers neural network
π₯ Benjamin Aubin, Antoine Maillard, Jean Barbier, Florent Krzakala, Nicolas Macris, Lenka ZdeborovΓ‘
π PDF
π Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors
π₯ Atsushi Nitanda, Taiji Suzuki
π PDF
π ServeNet: A Deep Neural Network for Web Service Classification
π₯ Yilong Yang, Peng Liu, Lianchao Ding, Bingqing Shen, Weiru Wang
π PDF
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π Learning Instance Segmentation by Interaction
π₯ Deepak Pathak, Yide Shentu, Dian Chen, Pulkit Agrawal, Trevor Darrell, Sergey Levine, Jitendra Malik
π PDF
π Quantizing deep convolutional networks for efficient inference: A whitepaper
π₯ Raghuraman Krishnamoorthi
π PDF
π Interpretable Discovery in Large Image Data Sets
π₯ Kiri L. Wagstaff, Jake Lee
π PDF
π Countdown Regression: Sharp and Calibrated Survival Predictions
π₯ Anand Avati, Tony Duan, Kenneth Jung, Nigam H. Shah, Andrew Ng
π PDF
π Fashion-Gen: The Generative Fashion Dataset and Challenge
π₯ Negar Rostamzadeh, Seyedarian Hosseini, Thomas Boquet, Wojciech Stokowiec, Ying Zhang, Christian Jauvin, Chris Pal
π PDF
π Online Saddle Point Problem with Applications to Constrained Online Convex Optimization
π₯ Adrian Rivera, He Wang, Huan Xu
π PDF
π Learning Graph Weighted Models on Pictures
π₯ Philip Amortila, Guillaume Rabusseau
π PDF
π How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments
π₯ CΓ©dric Colas, Olivier Sigaud, Pierre-Yves Oudeyer
π PDF
π Gated Complex Recurrent Neural Networks
π₯ Moritz Wolter, Angela Yao
π PDF
π Learning Transposition-Invariant Interval Features from Symbolic Music and Audio
π₯ Stefan Lattner, Maarten Grachten, Gerhard Widmer
π PDF
π DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures
π₯ Jin-Dong Dong, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun
π PDF
π Probabilistic PARAFAC2
π₯ Philip J. H. JΓΈrgensen, SΓΈren F. V. Nielsen, Jesper L. Hinrich, Mikkel N. Schmidt, Kristoffer H. Madsen, Morten MΓΈrup
π PDF
π Instance-Optimality in the Noisy Value-and Comparison-Model --- Accept, Accept, Strong Accept: Which Papers get in?
π₯ Vincent Cohen-Addad, Frederik Mallmann-Trenn, Claire Mathieu
π PDF
π Identifiability of Gaussian Structural Equation Models with Dependent Errors Having Equal Variances
π₯ Jose M. PeΓ±a
π PDF
π Robust and Efficient Boosting Method using the Conditional Risk
π₯ Zhi Xiao, Zhe Luo, Bo Zhong, Xin Dang
π PDF
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π Learning Instance Segmentation by Interaction
π₯ Deepak Pathak, Yide Shentu, Dian Chen, Pulkit Agrawal, Trevor Darrell, Sergey Levine, Jitendra Malik
π PDF
π Quantizing deep convolutional networks for efficient inference: A whitepaper
π₯ Raghuraman Krishnamoorthi
π PDF
π Interpretable Discovery in Large Image Data Sets
π₯ Kiri L. Wagstaff, Jake Lee
π PDF
π Countdown Regression: Sharp and Calibrated Survival Predictions
π₯ Anand Avati, Tony Duan, Kenneth Jung, Nigam H. Shah, Andrew Ng
π PDF
π Fashion-Gen: The Generative Fashion Dataset and Challenge
π₯ Negar Rostamzadeh, Seyedarian Hosseini, Thomas Boquet, Wojciech Stokowiec, Ying Zhang, Christian Jauvin, Chris Pal
π PDF
π Online Saddle Point Problem with Applications to Constrained Online Convex Optimization
π₯ Adrian Rivera, He Wang, Huan Xu
π PDF
π Learning Graph Weighted Models on Pictures
π₯ Philip Amortila, Guillaume Rabusseau
π PDF
π How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments
π₯ CΓ©dric Colas, Olivier Sigaud, Pierre-Yves Oudeyer
π PDF
π Gated Complex Recurrent Neural Networks
π₯ Moritz Wolter, Angela Yao
π PDF
π Learning Transposition-Invariant Interval Features from Symbolic Music and Audio
π₯ Stefan Lattner, Maarten Grachten, Gerhard Widmer
π PDF
π DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures
π₯ Jin-Dong Dong, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun
π PDF
π Probabilistic PARAFAC2
π₯ Philip J. H. JΓΈrgensen, SΓΈren F. V. Nielsen, Jesper L. Hinrich, Mikkel N. Schmidt, Kristoffer H. Madsen, Morten MΓΈrup
π PDF
π Instance-Optimality in the Noisy Value-and Comparison-Model --- Accept, Accept, Strong Accept: Which Papers get in?
π₯ Vincent Cohen-Addad, Frederik Mallmann-Trenn, Claire Mathieu
π PDF
π Identifiability of Gaussian Structural Equation Models with Dependent Errors Having Equal Variances
π₯ Jose M. PeΓ±a
π PDF
π Robust and Efficient Boosting Method using the Conditional Risk
π₯ Zhi Xiao, Zhe Luo, Bo Zhong, Xin Dang
π PDF
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π A Theory of Diagnostic Interpretation in Supervised Classification
π₯ Anirban Mukhopadhyay
π PDF
π Deep $k$-Means: Jointly Clustering with $k$-Means and Learning Representations
π₯ Maziar Moradi Fard, Thibaut Thonet, Eric Gaussier
π PDF
π Adaptive Blending Units: Trainable Activation Functions for Deep Neural Networks
π₯ Leon RenΓ© SΓΌtfeld, Flemming Brieger, Holger Finger, Sonja FΓΌllhase, Gordon Pipa
π PDF
π Adversarial Exploration Strategy for Self-Supervised Imitation Learning
π₯ Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee
π PDF
π The decoupled extended Kalman filter for dynamic exponential-family factorization models
π₯ Carlos Alberto Gomez-Uribe, Brian Karrer
π PDF
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π A Theory of Diagnostic Interpretation in Supervised Classification
π₯ Anirban Mukhopadhyay
π PDF
π Deep $k$-Means: Jointly Clustering with $k$-Means and Learning Representations
π₯ Maziar Moradi Fard, Thibaut Thonet, Eric Gaussier
π PDF
π Adaptive Blending Units: Trainable Activation Functions for Deep Neural Networks
π₯ Leon RenΓ© SΓΌtfeld, Flemming Brieger, Holger Finger, Sonja FΓΌllhase, Gordon Pipa
π PDF
π Adversarial Exploration Strategy for Self-Supervised Imitation Learning
π₯ Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee
π PDF
π The decoupled extended Kalman filter for dynamic exponential-family factorization models
π₯ Carlos Alberto Gomez-Uribe, Brian Karrer
π PDF
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π Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations
π₯ Dan Hendrycks, Thomas G. Dietterich
π PDF
π SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator
π₯ Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang
π PDF
π Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
π₯ Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee
π PDF
π Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization
π₯ Alexandre Laterre, Yunguan Fu, Mohamed Khalil Jabri, Alain-Sam Cohen, David Kas, Karl Hajjar, Torbjorn S. Dahl, Amine Kerkeni, Karim Beguir
π PDF
π Encoding Spatial Relations from Natural Language
π₯ Tiago Ramalho, TomΓ‘Ε‘ KociskΓ½, Frederic Besse, S. M. Ali Eslami, GΓ‘bor Melis, Fabio Viola, Phil Blunsom, Karl Moritz Hermann
π PDF
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π Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations
π₯ Dan Hendrycks, Thomas G. Dietterich
π PDF
π SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator
π₯ Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang
π PDF
π Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
π₯ Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee
π PDF
π Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization
π₯ Alexandre Laterre, Yunguan Fu, Mohamed Khalil Jabri, Alain-Sam Cohen, David Kas, Karl Hajjar, Torbjorn S. Dahl, Amine Kerkeni, Karim Beguir
π PDF
π Encoding Spatial Relations from Natural Language
π₯ Tiago Ramalho, TomΓ‘Ε‘ KociskΓ½, Frederic Besse, S. M. Ali Eslami, GΓ‘bor Melis, Fabio Viola, Phil Blunsom, Karl Moritz Hermann
π PDF
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π Kernel-Based Learning for Smart Inverter Control
π₯ Aditie Garg, Mana Jalali, Vassilis Kekatos, Nikolaos Gatsis
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π Is Q-learning Provably Efficient?
π₯ Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, Michael I. Jordan
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π Latent Alignment and Variational Attention
π₯ Yuntian Deng, Yoon Kim, Justin Chiu, Demi Guo, Alexander M. Rush
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π Representation Learning with Contrastive Predictive Coding
π₯ Aaron van den Oord, Yazhe Li, Oriol Vinyals
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π Scalable Sparse Subspace Clustering via Ordered Weighted $\ell_1$ Regression
π₯ Urvashi Oswal, Robert Nowak
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π Kernel-Based Learning for Smart Inverter Control
π₯ Aditie Garg, Mana Jalali, Vassilis Kekatos, Nikolaos Gatsis
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π Is Q-learning Provably Efficient?
π₯ Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, Michael I. Jordan
π PDF
π Latent Alignment and Variational Attention
π₯ Yuntian Deng, Yoon Kim, Justin Chiu, Demi Guo, Alexander M. Rush
π PDF
π Representation Learning with Contrastive Predictive Coding
π₯ Aaron van den Oord, Yazhe Li, Oriol Vinyals
π PDF
π Scalable Sparse Subspace Clustering via Ordered Weighted $\ell_1$ Regression
π₯ Urvashi Oswal, Robert Nowak
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π Scalable Tensor Completion with Nonconvex Regularization
π₯ Quanming Yao
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π NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference
π₯ Mahdi Nazemi, Ghasem Pasandi, Massoud Pedram
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π Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences
π₯ Jasper van der Waa, Jurriaan van Diggelen, Karel van den Bosch, Mark Neerincx
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π Understanding the Modeling of Computer Network Delays using Neural Networks
π₯ Albert Mestres, Eduard AlarcΓ³n, Yusheng Ji, Albert Cabellos-Aparicio
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π Auto-adaptive Resonance Equalization using Dilated Residual Networks
π₯ Maarten Grachten, Emmanuel Deruty, Alexandre Tanguy
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π Scalable Tensor Completion with Nonconvex Regularization
π₯ Quanming Yao
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π NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference
π₯ Mahdi Nazemi, Ghasem Pasandi, Massoud Pedram
π PDF
π Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences
π₯ Jasper van der Waa, Jurriaan van Diggelen, Karel van den Bosch, Mark Neerincx
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π Understanding the Modeling of Computer Network Delays using Neural Networks
π₯ Albert Mestres, Eduard AlarcΓ³n, Yusheng Ji, Albert Cabellos-Aparicio
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π Auto-adaptive Resonance Equalization using Dilated Residual Networks
π₯ Maarten Grachten, Emmanuel Deruty, Alexandre Tanguy
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Learning
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π Deep Structured Prediction with Nonlinear Output Transformations
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π PDF
π On the Geometry of Adversarial Examples
π₯ Marc Khoury, Dylan Hadfield-Menell
π PDF
π Minimizing Close-k Aggregate Loss Improves Classification
π₯ Bryan He, James Zou
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π The Natural Auditor: How To Tell If Someone Used Your Words To Train Their Model
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π PDF
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#Learning
AI Python & arXiv Channel