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
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