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#Learning
🗒 GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
👥 Zhilin Yang, Jake, Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann LeCun
📗 PDF
🗒 Self-Imitation Learning
👥 Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee
📗 PDF
🗒 Learning in POMDPs with Monte Carlo Tree Search
👥 Sammie Katt, Frans A. Oliehoek, Christopher Amato
📗 PDF
🗒 Stochastic Variance-Reduced Policy Gradient
👥 Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli
📗 PDF
🗒 Improving Consistency-Based Semi-Supervised Learning with Weight Averaging
👥 Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson
📗 PDF
🗒 Autoregressive Quantile Networks for Generative Modeling
👥 Georg Ostrovski, Will Dabney, Rémi Munos
📗 PDF
🗒 Improved Density-Based Spatio--Textual Clustering on Social Media
👥 Minh D. Nguyen, Won-Yong Shin
📗 PDF
🗒 The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing
👥 Cencheng Shen, Joshua T. Vogelstein
📗 PDF
🗒 NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical Usage
👥 Alexander Wong
📗 PDF
🗒 Neural Stethoscopes: Unifying Analytic, Auxiliary and Adversarial Network Probing
👥 Fabian B. Fuchs, Oliver Groth, Adam R. Kosoriek, Alex Bewley, Markus Wulfmeier, Andrea Vedaldi, Ingmar Posner
📗 PDF
🗒 Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
👥 Marton Havasi, José Miguel Hernández Lobato, Juan José Murillo Fuentes
📗 PDF
🗒 Low-rank geometric mean metric learning
👥 Mukul Bhutani, Pratik Jawanpuria, Hiroyuki Kasai, Bamdev Mishra
📗 PDF
🗒 The committee machine: Computational to statistical gaps in learning a two-layers neural network
👥 Benjamin Aubin, Antoine Maillard, Jean Barbier, Florent Krzakala, Nicolas Macris, Lenka Zdeborová
📗 PDF
🗒 Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors
👥 Atsushi Nitanda, Taiji Suzuki
📗 PDF
🗒 ServeNet: A Deep Neural Network for Web Service Classification
👥 Yilong Yang, Peng Liu, Lianchao Ding, Bingqing Shen, Weiru Wang
📗 PDF
#Learning
AI Python & arXiv Channel
Learning
#Learning
🗒 GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
👥 Zhilin Yang, Jake, Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann LeCun
🗒 Self-Imitation Learning
👥 Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee
🗒 Learning in POMDPs with Monte Carlo Tree Search
👥 Sammie Katt, Frans A. Oliehoek, Christopher Amato
🗒 Stochastic Variance-Reduced Policy Gradient
👥 Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli
🗒 Improving Consistency-Based Semi-Supervised Learning with Weight Averaging
👥 Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson
🗒 Autoregressive Quantile Networks for Generative Modeling
👥 Georg Ostrovski, Will Dabney, Rémi Munos
🗒 Improved Density-Based Spatio--Textual Clustering on Social Media
👥 Minh D. Nguyen, Won-Yong Shin
🗒 The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing
👥 Cencheng Shen, Joshua T. Vogelstein
🗒 NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical Usage
👥 Alexander Wong
🗒 Neural Stethoscopes: Unifying Analytic, Auxiliary and Adversarial Network Probing
👥 Fabian B. Fuchs, Oliver Groth, Adam R. Kosoriek, Alex Bewley, Markus Wulfmeier, Andrea Vedaldi, Ingmar Posner
🗒 Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
👥 Marton Havasi, José Miguel Hernández Lobato, Juan José Murillo Fuentes
🗒 Low-rank geometric mean metric learning
👥 Mukul Bhutani, Pratik Jawanpuria, Hiroyuki Kasai, Bamdev Mishra
🗒 The committee machine: Computational to statistical gaps in learning a two-layers neural network
👥 Benjamin Aubin, Antoine Maillard, Jean Barbier, Florent Krzakala, Nicolas Macris, Lenka Zdeborová
🗒 Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors
👥 Atsushi Nitanda, Taiji Suzuki
🗒 ServeNet: A Deep Neural Network for Web Service Classification
👥 Yilong Yang, Peng Liu, Lianchao Ding, Bingqing Shen, Weiru Wang
#Learning
AI Python & arXiv Channel