Poster contributions Speculate-Correct Error Bounds for Local Classifiers Eric Bax*, Verizon maaGMA: Optimizing a Multi-Task Generator Against Several Discriminator Networks Sahil Chopra*, Stanford University; Ryan Holmdahl, Stanford University Analyzing global urbanization with remote-sensing data and generative adversarial networks Adrian Albert*, MIT; Emanuele Strano, Massachusetts Institute of Technology; Marta Gonzalez, MIT Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization Fabian Pedregosa*, UC Berkeley Learning Supervised Binary Hashing without Binary Code Optimization Miguel Carreira-Perpinan*, UC Merced; Ramin Raziperchikolaei, UC Merced Fusing Side Information for Transfer Learning Yao-Hung Tsai*, Carnegie Mellon University; Ruslan Salakhutdinov, Carnegie Mellon University A Relaxation Perspective on Policy Optimization Daniel Levy*, Stanford University; Stefano Ermon, Stanford University Iterative Refinement for Machine Translation Roman Novak*, Google; Michael Auli, Facebook; David Grangier, Facebook Stochastic Gradient Descent: Going As Fast As Possible But Not Faster Alice Schoenauer-Sebag*, UCSF; Marc Schoenauer, INRIA; Michèle Sebag, CNRS Deep Character-Level Click-Through Rate Prediction for Sponsored Search Amin Mantrach*, Criteo; Bora Edizel, UPF; Xiao Bai, Yahoo Off-Policy Actor-Critic with Function Approximation for Bidding in Computational Advertising Hamid Maei*, Criteo Learning with Abandonment Sven Schmit*, Stanford University; Ramesh Johari, Stanford University Gaussian Prototypical Networks for Few-Shot Learning on Omniglot Stanislav Fort*, Stanford University Adversarial Spheres: Exploring Adversarial Examples on a Simple Dataset Justin Gilmer*, Google Brain; Fartash Faghri, University of Toronto; Luke Metz, Google Brain; Maithra Raghu, Google Brain/ Cornell; Ian Goodfellow, Google Brain Learning Dialog Policy in End-to-End Task-Oriented Neural Dialog Models Bing Liu*, Carnegie Mellon University; Ian Lane, Carnegie Mellon University Causal Generative Neural Networks Isabelle Guyon*, UPSud, INRIA, University Paris-saclay and ChaLearn GCN-LSTM Framework For Real-Time Macroscopic Traffic Congestion Prediction Sudatta Mohanty*, UC Berkeley; Alexei Pozdnukhov, Sidewalk Labs Active Learning for Deep Convolutional Neural Networks using Dropout Armin Kappeler*, Oath The Effects of Memory Replay in Reinforcement Learning Ruishan Liu*, Stanford University; James Zou, Microsoft Active Learning for Training Deep Neural Networks Tai-Peng Tian*, Apple Inc.; Wenda Wang, Apple Inc.; Yin Zhou, Apple Inc.; Oncel Tuzel, Apple Inc. Deep Simultaneous Localization and Mapping Emilio Parisotto, Carnegie Mellon University; Devendra Singh Chaplot, Carnegie Mellon University; Jian Zhang, Apple Inc.; Ruslan Salakhutdinov*, Carnegie Mellon University Neural Program Synthesis with Policy Gradient Daniel Abolafia*, Google Brain; Quoc Le, Google Brain; Mohammad Norouzi, Google Learning from Simulated and Unsupervised Images through Adversarial Training Ashish Shrivastava, Apple; Tomas Pfister, Apple; Oncel Tuzel*, Apple; Josh Susskind, Apple; Wenda Wang, Apple Inc.; Russ Webb, Apple Multi-Objective Optimization for Dynamic Pricing in the On-Demand Economy Aayush Gupta*, Saratoga High School Why adaptively collected data have negative bias and how to correct for it. Xinkun Nie*, Stanford University; Xiaoying Tian, Stanford University; Jonathan Taylor, Stanford University; James Zou, Stanford University Verifying Properties of Binarized Neural Networks Shiva Kasiviswanathan, Samsung Research; Nina Narodytska*, VMware; Leonid Ryzhyk, VmWare; Mooly Sagiv, VMware; Toby Walsh, UNSW Deep Lattice Networks and Partial Monotonic Functions Maya Gupta*, Google Deep Multiple Instance Feature Learning via Variational Autoencoder Nanxiang Li*; Shabnam Ghaffarzadegan Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? Maithra Raghu*, Google Brain/ Cornell Towards a cosmology emulator using Generative Adversarial Networks Mustafa Mustafa*, Berkeley Lab Deep Gaussian Processes and Deep Neural Networks Jaehoon Lee*, Google Brain; Yasaman Bahri, Google Brain Improving transfer using augmented feedback in Progressive Neural Networks Deepika Bablani*, Carnegie Mellon University; Parth Chadha, CMU Simplicity and Generalization in Deep Neural Networks Roman Novak*, Google; Jascha Sohl-Dickstein, Google Brain; Dan Abolafia, Google Brain Jeffrey Pennington, Google Brain; Yasaman Bahri, Google Brain; TransFlow: Unsupervised Motion Flow by Joint Geometric and Pixel-level Estimation Luca Rigazio*, Panasonic Silicon Valley Laboratory; Stefano Alletto, Unimore Submission abstracts Download all (18Mb) | |||||||||||||||||||||||||||||||||||||||||||||
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