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6th ICLR 2018: Vancouver, BC, Canada: Worshop Track
- 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Workshop Track Proceedings. OpenReview.net 2018
Accepted Papers
- Yuhui Yuan, Kuiyuan Yang, Jianyuan Guo, Chao Zhang, Jingdong Wang:
Feature Incay for Representation Regularization. - Joshua C. Peterson, Krisha Aghi, Jordan W. Suchow, Alexander Y. Ku, Tom Griffiths:
Capturing Human Category Representations by Sampling in Deep Feature Spaces. - Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Alyosha A. Efros, Thomas L. Griffiths:
Investigating Human Priors for Playing Video Games. - George Philipp, Dawn Song, Jaime G. Carbonell:
Gradients explode - Deep Networks are shallow - ResNet explained. - Sanjeev Arora, Elad Hazan, Holden Lee, Karan Singh, Cyril Zhang, Yi Zhang:
Towards Provable Control for Unknown Linear Dynamical Systems. - Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, Ákos Kádár, Adam Trischler, Yoshua Bengio:
FigureQA: An Annotated Figure Dataset for Visual Reasoning. - Keyi Yu, Yang Liu, Alexander G. Schwing, Jian Peng:
Fast and Accurate Text Classification: Skimming, Rereading and Early Stopping. - Steven T. Kothen-Hill, Asaf Zviran, Rafael C. Schulman, Sunil Deochand, Federico Gaiti, Dillon Maloney, Kevin Y. Huang, Will Liao, Nicolas Robine, Nathaniel D. Omans, Dan A. Landau:
Deep learning mutation prediction enables early stage lung cancer detection in liquid biopsy. - Qibin Zhao, Masashi Sugiyama, Longhao Yuan, Andrzej Cichocki:
Learning Efficient Tensor Representations with Ring Structure Networks. - Yi Wu, Yuxin Wu, Georgia Gkioxari, Yuandong Tian:
Building Generalizable Agents with a Realistic and Rich 3D Environment. - Oliver Hennigh:
Automated Design using Neural Networks and Gradient Descent. - Michael Zhu, Suyog Gupta:
To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression. - Robert S. DiPietro, Christian Rupprecht, Nassir Navab, Gregory D. Hager:
Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies. - Illia Polosukhin, Alexander Skidanov:
Neural Program Search: Solving Programming Tasks from Description and Examples. - Oleg Rybakov, Vijai Mohan, Avishkar Misra, Scott LeGrand, Rejith Joseph, Kiuk Chung, Siddharth Singh, Qian You, Eric T. Nalisnick, Leo Dirac, Runfei Luo:
The Effectiveness of a two-Layer Neural Network for Recommendations. - Tiago Pimentel, Adriano Veloso, Nivio Ziviani:
Fast Node Embeddings: Learning Ego-Centric Representations. - Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc V. Le, Jon M. Kleinberg:
Can Deep Reinforcement Learning solve Erdos-Selfridge-Spencer Games? - Daniel Neil, Marwin H. S. Segler, Laura Guasch, Mohamed Ahmed, Dean Plumbley, Matthew Sellwood, Nathan Brown:
Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design. - David Madras, Toniann Pitassi, Richard S. Zemel:
Predict Responsibly: Increasing Fairness by Learning to Defer. - Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow:
Adversarial Spheres. - Ben Usman, Kate Saenko, Brian Kulis:
Stable Distribution Alignment Using the Dual of the Adversarial Distance. - Yang Li, Nan Du, Samy Bengio:
Time-Dependent Representation for Neural Event Sequence Prediction. - Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra:
Distributional Adversarial Networks. - Han Zhao, Shanghang Zhang, Guanhang Wu, João Paulo Costeira, José M. F. Moura, Geoffrey J. Gordon:
Multiple Source Domain Adaptation with Adversarial Learning. - Martin Schrimpf, Stephen Merity, James Bradbury, Richard Socher:
A Flexible Approach to Automated RNN Architecture Generation. - Thomas Elsken, Jan Hendrik Metzen, Frank Hutter:
Simple and efficient architecture search for Convolutional Neural Networks. - Shaojie Bai, J. Zico Kolter, Vladlen Koltun:
Convolutional Sequence Modeling Revisited. - Vitalii Zhelezniak, Dan Busbridge, April Shen, Samuel L. Smith, Nils Y. Hammerla:
Decoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity Tasks. - Prajit Ramachandran, Barret Zoph, Quoc V. Le:
Searching for Activation Functions. - Yoav Levine, Or Sharir, Amnon Shashua:
Benefits of Depth for Long-Term Memory of Recurrent Networks. - Bowen Baker, Otkrist Gupta, Ramesh Raskar, Nikhil Naik:
Accelerating Neural Architecture Search using Performance Prediction. - Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean:
Faster Discovery of Neural Architectures by Searching for Paths in a Large Model. - Richard Wei, Lane Schwartz, Vikram S. Adve:
DLVM: A modern compiler infrastructure for deep learning systems. - Chao Gao, Martin Müller, Ryan Hayward:
Adversarial Policy Gradient for Alternating Markov Games. - Yusuke Tsuzuku, Hiroto Imachi, Takuya Akiba:
Variance-based Gradient Compression for Efficient Distributed Deep Learning. - Tom Zahavy, Bingyi Kang, Alex Sivak, Jiashi Feng, Huan Xu, Shie Mannor:
Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms. - Cijo Jose, Moustapha Cissé, François Fleuret:
Kronecker Recurrent Units. - Shohei Ohsawa, Kei Akuzawa, Tatsuya Matsushima, Gustavo Bezerra, Yusuke Iwasawa, Hiroshi Kajino, Seiya Takenaka, Yutaka Matsuo:
Neuron as an Agent. - Ekin Dogus Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le:
Intriguing Properties of Adversarial Examples. - Chenwei Wu, Jiajun Luo, Jason D. Lee:
No Spurious Local Minima in a Two Hidden Unit ReLU Network. - Beidi Chen, Yingchen Xu, Anshumali Shrivastava:
Lsh-Sampling breaks the Computational chicken-and-egg Loop in adaptive stochastic Gradient estimation. - Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas:
Learning Representations and Generative Models for 3D Point Clouds. - Anuroop Sriram, Heewoo Jun, Sanjeev Satheesh, Adam Coates:
Cold fusion: Training Seq2seq Models Together with Language Models. - Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, Yoshua Bengio:
Extending the Framework of Equilibrium Propagation to General Dynamics. - Maher Nouiehed, Meisam Razaviyayn:
Learning Deep Models: Critical Points and Local Openness. - Risi Kondor, Hy Truong Son, Horace Pan, Brandon M. Anderson, Shubhendu Trivedi:
Covariant Compositional Networks For Learning Graphs. - Levent Sagun, Utku Evci, V. Ugur Güney, Yann N. Dauphin, Léon Bottou:
Empirical Analysis of the Hessian of Over-Parametrized Neural Networks. - Zhendong Zhang, Cheolkon Jung:
Regularization Neural Networks via Constrained Virtual Movement Field. - Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard S. Zemel:
Graph Partition Neural Networks for Semi-Supervised Classification. - Quynh Nguyen, Matthias Hein:
The loss surface and expressivity of deep convolutional neural networks. - Brandon Reagen, Udit Gupta, Robert Adolf, Michael Mitzenmacher, Alexander M. Rush
, Gu-Yeon Wei, David Brooks:
Weightless: Lossy weight encoding for deep neural network compression. - Daniel Soudry, Elad Hoffer:
Exponentially vanishing sub-optimal local minima in multilayer neural networks. - Xinyun Chen, Chang Liu, Dawn Song:
Tree-to-tree Neural Networks for Program Translation. - Wenpeng Hu, Bing Liu, Jinwen Ma, Dongyan Zhao, Rui Yan:
Aspect-based Question Generation. - Lars Hiller Eidnes, Arild Nøkland:
Shifting Mean Activation Towards Zero with Bipolar Activation Functions. - Attila Szabó, Qiyang Hu, Tiziano Portenier, Matthias Zwicker, Paolo Favaro:
Challenges in Disentangling Independent Factors of Variation. - Fan Yang, Jiazhong Nie, William W. Cohen, Ni Lao:
Learning to Organize Knowledge with N-Gram Machines. - Rumen Dangovski, Li Jing, Marin Soljacic:
Rotational Unit of Memory. - Taihong Xiao, Jiapeng Hong, Jinwen Ma:
DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images. - Yasin Yazici, Kim-Hui Yap, Stefan Winkler:
Autoregressive Generative Adversarial Networks. - Peter H. Jin, Sergey Levine, Kurt Keutzer:
Regret Minimization for Partially Observable Deep Reinforcement Learning. - Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong:
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations. - Zichao Long, Yiping Lu, Xianzhong Ma, Bin Dong:
PDE-Net: Learning PDEs from Data. - Yang Gao, Huazhe Xu, Ji Lin, Fisher Yu, Sergey Levine, Trevor Darrell:
Reinforcement Learning from Imperfect Demonstrations. - Daniel Li, Asim Kadav:
Adaptive Memory Networks. - Gabriel Huang, Hugo Berard, Ahmed Touati, Gauthier Gidel, Pascal Vincent, Simon Lacoste-Julien:
Parametric Adversarial Divergences are Good Task Losses for Generative Modeling. - Joseph Marino, Yisong Yue, Stephan Mandt:
Learning to Infer. - Alessandro Bay, Biswa Sengupta:
GeoSeq2Seq: Information Geometric Sequence-to-Sequence Networks. - Guillaume Alain, Nicolas Le Roux, Pierre-Antoine Manzagol:
Negative eigenvalues of the Hessian in deep neural networks. - Chong Yu, Young Wang:
3D-Scene-GAN: Three-dimensional Scene Reconstruction with Generative Adversarial Networks. - Daniel Fojo, Víctor Campos, Xavier Giró-i-Nieto:
Comparing Fixed and Adaptive Computation Time for Recurrent Neural Networks. - Shikhar Sharma, Dendi Suhubdy, Vincent Michalski, Samira Ebrahimi Kahou, Yoshua Bengio:
ChatPainter: Improving Text to Image Generation using Dialogue. - Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao:
ReinforceWalk: Learning to Walk in Graph with Monte Carlo Tree Search. - Charles B. Delahunt, J. Nathan Kutz:
A moth brain learns to read MNIST. - Sil C. van de Leemput, Jonas Teuwen, Rashindra Manniesing:
MemCNN: a Framework for Developing Memory Efficient Deep Invertible Networks. - Raghav Goyal, Farzaneh Mahdisoltani, Guillaume Berger, Waseem Gharbieh, Ingo Bax, Roland Memisevic:
Evaluating visual "common sense" using fine-grained classification and captioning tasks. - Mehran Pesteie, Purang Abolmaesumi, Robert Rohling:
Deep Neural Maps. - Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf:
Clustering Meets Implicit Generative Models. - Anuvabh Dutt, Denis Pellerin, Georges Quénot:
Coupled Ensembles of Neural Networks. - Yedid Hoshen, Lior Wolf:
NAM - Unsupervised Cross-Domain Image Mapping without Cycles or GANs. - Nathan H. Ng, Julian J. McAuley, Julian Gingold, Nina Desai, Zachary C. Lipton:
Predicting Embryo Morphokinetics in Videos with Late Fusion Nets & Dynamic Decoders. - Richard Shin, Charles Packer, Dawn Song:
Differentiable Neural Network Architecture Search. - Dilin Wang, Qiang Liu:
An Optimization View on Dynamic Routing Between Capsules. - Yun Chen, Kyunghyun Cho, Samuel R. Bowman, Victor O. K. Li:
Stable and Effective Trainable Greedy Decoding for Sequence to Sequence Learning. - Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf:
Tempered Adversarial Networks. - Joshua Romoff, Alexandre Piché, Peter Henderson, Vincent François-Lavet, Joelle Pineau:
Reward Estimation for Variance Reduction in Deep Reinforcement Learning. - Natasha Jaques, Jesse H. Engel, David Ha, Fred Bertsch, Rosalind W. Picard, Douglas Eck:
Learning via social awareness: improving sketch representations with facial feedback. - Siyu He, Siamak Ravanbakhsh, Shirley Ho:
Analysis of Cosmic Microwave Background with Deep Learning. - Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Pushmeet Kohli, Edward Grefenstette:
Jointly Learning "What" and "How" from Instructions and Goal-States. - Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov:
Uncertainty Estimation via Stochastic Batch Normalization. - Hiroaki Shioya, Yusuke Iwasawa, Yutaka Matsuo:
Extending Robust Adversarial Reinforcement Learning Considering Adaptation and Diversity. - Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Raquel Urtasun, Richard S. Zemel:
Leveraging Constraint Logic Programming for Neural Guided Program Synthesis. - Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon:
Multi-Agent Generative Adversarial Imitation Learning. - Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin:
Learning Disentangled Representations with Wasserstein Auto-Encoders. - Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, Han-Lim Choi:
Adaptive Path-Integral Approach for Representation Learning and Planning. - Yiming Zhang, Quan Ho Vuong, Kenny Song, Xiao-Yue Gong, Keith W. Ross:
Efficient Entropy For Policy Gradient with Multi-Dimensional Action Space. - Samantha Guerriero, Barbara Caputo, Thomas Mensink:
DeepNCM: Deep Nearest Class Mean Classifiers. - Matan Haroush, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor:
Learning How Not to Act in Text-based Games. - Kangwook Lee, Kyungmin Lee, Hoon Kim, Changho Suh, Kannan Ramchandran:
SGD on Random Mixtures: Private Machine Learning under Data Breach Threats. - Zheng Xu, Yen-Chang Hsu, Jiawei Huang:
Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks. - Jin-Dong Dong, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun:
PPP-Net: Platform-aware Progressive Search for Pareto-optimal Neural Architectures. - Joji Toyama, Yusuke Iwasawa, Kotaro Nakayama, Yutaka Matsuo:
Expert-based reward function training: the novel method to train sequence generators. - Tianyun Zhang, Shaokai Ye, Yipeng Zhang, Yanzhi Wang, Makan Fardad:
Systematic Weight Pruning of DNNs using Alternating Direction Method of Multipliers. - Abdul Rahman Abdul Ghani, Nishanth Koganti, Alfredo Solano, Yusuke Iwasawa, Kotaro Nakayama, Yutaka Matsuo:
Designing Efficient Neural Attention Systems Towards Achieving Human-level Sharp Vision. - Feng Wang, Weiyang Liu, Hanjun Dai, Haijun Liu, Jian Cheng:
Additive Margin Softmax for Face Verification. - Asha Anoosheh, Eirikur Agustsson, Radu Timofte:
ComboGAN: Unrestricted Scalability for Image Domain Translation. - Xavier Bresson, Thomas Laurent:
An Experimental Study of Neural Networks for Variable Graphs. - Rinu Boney, Alexander Ilin:
Semi-Supervised Few-Shot Learning with MAML. - Yuechao Gao, Nianhong Liu, Sheng Zhang:
Stacked Filters Stationary Flow For Hardware-Oriented Acceleration Of Deep Convolutional Neural Networks. - Yoshihiro Yamada, Masakazu Iwamura, Koichi Kise:
ShakeDrop regularization. - Fei Wang, Tiark Rompf:
A Language and Compiler View on Differentiable Programming. - David Lopez-Paz, Levent Sagun:
Easing non-convex optimization with neural networks. - Maxwell I. Nye, Andrew Saxe:
Are Efficient Deep Representations Learnable? - Trieu H. Trinh, Andrew M. Dai, Minh-Thang Luong, Quoc V. Le:
Learning Longer-term Dependencies in RNNs with Auxiliary Losses. - Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Chun-Yi Lee:
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning. - Jiajin Li, Baoxiang Wang:
Policy Optimization with Second-Order Advantage Information. - Yulia Rubanova, Ruian Shi, Roujia Li, Jeff Wintersinger, Amit G. Deshwar, Nil Sahin, Quaid Morris:
Reconstructing evolutionary trajectories of mutations in cancer. - Thomas Wolf, Julien Chaumond, Clement Delangue:
Meta-Learning a Dynamical Language Model. - Noe Casas, José A. R. Fonollosa, Marta R. Costa-jussà:
A differentiable BLEU loss. Analysis and first results. - Marek Krcál, Ondrej Svec, Martin Bálek, Otakar Jasek:
Deep Convolutional Malware Classifiers Can Learn from Raw Executables and Labels Only. - Yusuke Iwasawa, Kotaro Nakayama, Yutaka Matsuo:
Censoring Representations with Multiple-Adversaries over Random Subspaces. - Stanislaw Jastrzebski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos J. Storkey:
Finding Flatter Minima with SGD. - Siddhartha Brahma:
SufiSent - Universal Sentence Representations Using Suffix Encodings. - Chen Ma, Junfeng Wen, Yoshua Bengio:
Universal Successor Representations for Transfer Reinforcement Learning. - Namrata Anand, Possu Huang:
Generative Modeling for Protein Structures. - Jakob N. Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktäschel, Eric P. Xing, Shimon Whiteson:
DiCE: The Infinitely Differentiable Monte-Carlo Estimator. - Sachin Ravi, Hugo Larochelle:
Meta-Learning for Batch Mode Active Learning. - Soorya Gopalakrishnan, Zhinus Marzi, Upamanyu Madhow, Ramtin Pedarsani:
Combating Adversarial Attacks Using Sparse Representations. - César Laurent, Thomas George, Xavier Bouthillier, Nicolas Ballas, Pascal Vincent:
An Evaluation of Fisher Approximations Beyond Kronecker Factorization. - David Pfau, Christopher P. Burgess:
Minimally Redundant Laplacian Eigenmaps. - Chris Donahue, Julian J. McAuley, Miller S. Puckette:
Synthesizing Audio with GANs. - Mateo Rojas-Carulla, Marco Baroni, David Lopez-Paz:
Causal Discovery Using Proxy Variables. - Sam Leroux, Pavlo Molchanov, Pieter Simoens, Bart Dhoedt, Thomas M. Breuel, Jan Kautz:
IamNN: Iterative and Adaptive Mobile Neural Network for efficient image classification. - Robert J. Wang, Xiang Li, Shuang Ao, Charles X. Ling:
Pelee: A Real-Time Object Detection System on Mobile Devices. - Yingzhen Yang, Jianchao Yang, Ning Xu, Wei Han, Nebojsa Jojic, Thomas S. Huang:
3D-FilterMap: A Compact Architecture for Deep Convolutional Neural Networks. - Ciprian Florescu, Christian Igel:
Resilient Backpropagation (Rprop) for Batch-learning in TensorFlow. - Mika Sarkin Jain, Jack Lindsey:
Semiparametric Reinforcement Learning. - Cheng-Zhi Anna Huang, Sherol Chen, Mark J. Nelson, Douglas Eck:
Towards Mixed-initiative generation of multi-channel sequential structure. - Tian Guo, Tao Lin, Yao Lu:
An interpretable LSTM neural network for autoregressive exogenous model. - Kamil Bennani-Smires, Claudiu Musat, Andreea Hossmann, Michael Baeriswyl:
GitGraph - from Computational Subgraphs to Smaller Architecture Search Spaces. - Alexander A. Alemi, Ian Fischer:
GILBO: One Metric to Measure Them All. - Igor Mordatch:
Concept Learning with Energy-Based Models. - Ashutosh Kumar, Arijit Biswas, Subhajit Sanyal:
eCommerceGAN: A Generative Adversarial Network for e-commerce. - Richard Shin, Illia Polosukhin, Dawn Song:
Towards Specification-Directed Program Repair. - Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim:
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. - Fisher Yu, Dequan Wang, Evan Shelhamer, Trevor Darrell:
Learning Rich Image Representation with Deep Layer Aggregation. - Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Ruslan Salakhutdinov:
LSTM Iteration Networks: An Exploration of Differentiable Path Finding. - Peter H. Jin, Boris Ginsburg, Kurt Keutzer:
Spatially Parallel Convolutions. - Amy Zhang, Harsh Satija, Joelle Pineau:
Decoupling Dynamics and Reward for Transfer Learning. - Pei-Hsuan Lu, Pin-Yu Chen, Chia-Mu Yu:
On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples. - Edgar Minasyan, Vinay Prabhu:
Hockey-Stick GAN. - Ivan Lobov:
SpectralWords: Spectral Embeddings Approach to Word Similarity Task for Large Vocabularies. - Shiyu Liang, Ruoyu Sun, Yixuan Li, R. Srikant:
Understanding the Loss Surface of Single-Layered Neural Networks for Binary Classification. - Yash Sharma, Pin-Yu Chen:
Attacking the Madry Defense Model with $L_1$-based Adversarial Examples. - Arjun Nitin Bhagoji, Warren He, Bo Li, Dawn Song:
Black-box Attacks on Deep Neural Networks via Gradient Estimation. - Zhe Li, Shuo Wang, Caiwen Ding, Qinru Qiu, Yanzhi Wang, Yun Liang:
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs. - Phiala E. Shanahan, Daniel Trewartha, William Detmold:
Neural network parameter regression for lattice quantum chromodynamics simulations in nuclear and particle physics. - Zachary Nado, Jasper Snoek, Roger B. Grosse, David Duvenaud, Bowen Xu, James Martens:
Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure. - Remi Tachet des Combes, Philip Bachman, Harm van Seijen:
Learning Invariances for Policy Generalization. - Lionel Gueguen, Alex Sergeev, Rosanne Liu, Jason Yosinski:
Faster Neural Networks Straight from JPEG. - KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard S. Zemel, Xaq Pitkow:
Inference in probabilistic graphical models by Graph Neural Networks. - Tim Tsz-Kit Lau, Jinshan Zeng, Baoyuan Wu, Yuan Yao:
A Proximal Block Coordinate Descent Algorithm for Deep Neural Network Training. - Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow:
Realistic Evaluation of Semi-Supervised Learning Algorithms. - Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein:
Learning to Learn Without Labels. - Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alyosha A. Efros, Sergey Levine:
Conditional Networks for Few-Shot Semantic Segmentation. - Stefan Falkner, Aaron Klein, Frank Hutter:
Practical Hyperparameter Optimization for Deep Learning. - George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine:
The Mirage of Action-Dependent Baselines in Reinforcement Learning. - Nicolas Le Roux, Reza Babanezhad, Pierre-Antoine Manzagol:
Online variance-reducing optimization. - Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle, Aaron C. Courville:
HoME: a Household Multimodal Environment. - Alexander Chistyakov, Ekaterina Lobacheva, Alexander Shevelev, Alexey Romanenko:
Monotonic models for real-time dynamic malware detection. - Romain Laroche, Harm van Seijen:
In reinforcement learning, all objective functions are not equal. - Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar:
Compression by the signs: distributed learning is a two-way street. - Rui Shu, Shengjia Zhao, Mykel J. Kochenderfer:
Rethinking Style and Content Disentanglement in Variational Autoencoders. - Max Kochurov, Timur Garipov, Dmitry Podoprikhin, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov:
Bayesian Incremental Learning for Deep Neural Networks. - Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin:
Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders. - Samuli Laine:
Feature-Based Metrics for Exploring the Latent Space of Generative Models. - Ryan Spring, Anshumali Shrivastava:
Scalable Estimation via LSH Samplers (LSS). - Roland Fernandez, Asli Celikyilmaz, Paul Smolensky, Rishabh Singh:
Learning and Analyzing Vector Encoding of Symbolic Representation. - Ysbrand Galama, Thomas Mensink:
Iterative GANs for Rotating Visual Objects. - Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel:
PixelSNAIL: An Improved Autoregressive Generative Model. - Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud:
Isolating Sources of Disentanglement in Variational Autoencoders. - Rose Catherine, William W. Cohen:
TransNets for Review Generation. - Martin Simonovsky, Nikos Komodakis:
Towards Variational Generation of Small Graphs. - Yao-Hung Hubert Tsai, Denny Wu, Makoto Yamada, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu:
Selecting the Best in GANs Family: a Post Selection Inference Framework. - Andreea Bobu, Eric Tzeng, Judy Hoffman
Adapting to Continuously Shifting Domains. - Will Grathwohl, Elliot Creager, Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel:
Gradient-based Optimization of Neural Network Architecture. - Facundo Sapienza, Pablo Groisman, Matthieu Jonckheere:
Weighted Geodesic Distance Following Fermat's Principle. - Satrajit Chatterjee:
Learning and Memorization. - Bruno Lecouat, Chuan Sheng Foo, Houssam Zenati, Vijay Ramaseshan Chandrasekhar:
Semi-Supervised Learning With GANs: Revisiting Manifold Regularization. - Anna T. Thomas, Albert Gu, Tri Dao, Atri Rudra, Christopher Ré:
Learning Invariance with Compact Transforms. - Amit Deshpande, Navin Goyal, Sushrut Karmalkar:
Depth separation and weight-width trade-offs for sigmoidal neural networks. - Ryan Szeto, Simon Stent, Germán Ros, Jason J. Corso:
A Dataset To Evaluate The Representations Learned By Video Prediction Models. - Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, Sergey Levine:
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning. - Damien Scieur, Edouard Oyallon, Alexandre d'Aspremont, Francis R. Bach:
Nonlinear Acceleration of CNNs. - D. Sculley, Jasper Snoek, Alexander B. Wiltschko, Ali Rahimi:
Winner's Curse? On Pace, Progress, and Empirical Rigor.
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