Papers
Gradually Updated Neural Networks for Large-Scale Image Recognition
Siyuan Qiao, Zhishuai Zhang, Wei Shen et al.
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation
Qiang Sun, Kean Ming Tan, Han Liu et al.
Graph Networks as Learnable Physics Engines for Inference and Control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg et al.
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Jiaxuan You, Rex Ying, Xiang Ren et al.
Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions
Wenruo Bai, Jeff Bilmes
Hierarchical Clustering with Structural Constraints
Vaggos Chatziafratis, Rad Niazadeh, Moses Charikar
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
Zhengping Che, Sanjay Purushotham, Guangyu Li et al.
Hierarchical Imitation and Reinforcement Learning
Hoang Le, Nan Jiang, Alekh Agarwal et al.
Hierarchical Long-term Video Prediction without Supervision
Nevan wichers, Ruben Villegas, Dumitru Erhan et al.
Hierarchical Multi-Label Classification Networks
Jonatas Wehrmann, Ricardo Cerri, Rodrigo Barros
Hierarchical Text Generation and Planning for Strategic Dialogue
Denis Yarats, Mike Lewis
High Performance Zero-Memory Overhead Direct Convolutions
Jiyuan Zhang, Franz Franchetti, Tze Meng Low
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
Tim Pearce, Alexandra Brintrup, Mohamed Zaki et al.
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Octavian Ganea, Gary Becigneul, Thomas Hofmann
Image Transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit et al.
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
Lasse Espeholt, Hubert Soyer, Remi Munos et al.
Implicit Quantile Networks for Distributional Reinforcement Learning
Will Dabney, Georg Ostrovski, David Silver et al.
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion
Cong Ma, Kaizheng Wang, Yuejie Chi et al.
Importance Weighted Transfer of Samples in Reinforcement Learning
Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta et al.
Improved large-scale graph learning through ridge spectral sparsification
Daniele Calandriello, Alessandro Lazaric, Ioannis Koutis et al.
Improved nearest neighbor search using auxiliary information and priority functions
Omid Keivani, Kaushik Sinha
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems
Marc Abeille, Alessandro Lazaric
Improved Training of Generative Adversarial Networks Using Representative Features
Duhyeon Bang, Hyunjung Shim
Improving Optimization for Models With Continuous Symmetry Breaking
Robert Bamler, Stephan Mandt
Improving Regression Performance with Distributional Losses
Ehsan Imani, Martha White