Papers
11,951 papers found
Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning
Tianmin Shu, Caiming Xiong, Richard Socher
Hierarchical Density Order Embeddings
Ben Athiwaratkun, Andrew Gordon Wilson
Hierarchical Graph Structure Learning for Multi-View 3D Model Retrieval
Yuting Su, Wenhui Li, Anan Liu et al.
Hierarchical Representations for Efficient Architecture Search
Hanxiao Liu, Karen Simonyan, Oriol Vinyals et al.
Hierarchical Subtask Discovery with Non-Negative Matrix Factorization
Adam C. Earle, Andrew M. Saxe, Benjamin Rosman
Hyperparameter optimization: a spectral approach
Elad Hazan, Adam Klivans, Yang Yuan
Identifying Analogies Across Domains
Yedid Hoshen, Lior Wolf
Imitation Learning from Visual Data with Multiple Intentions
Aviv Tamar, Khashayar Rohanimanesh, Yinlam Chow et al.
Implicit Causal Models for Genome-wide Association Studies
Dustin Tran, David M. Blei
Improving GANs Using Optimal Transport
Tim Salimans, Han Zhang, Alec Radford et al.
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization
Yanshuai Cao, Gavin Weiguang Ding, Kry Yik-Chau Lui et al.
Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect
Xiang Wei, Boqing Gong, Zixia Liu et al.
Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction
Da Xiao, Jo-Yu Liao, Xingyuan Yuan
Initialization matters: Orthogonal Predictive State Recurrent Neural Networks
Krzysztof Choromanski, Carlton Downey, Byron Boots
Interactive Grounded Language Acquisition and Generalization in a 2D World
Haonan Yu, Haichao Zhang, Wei Xu
Interpretable Counting for Visual Question Answering
Alexander Trott, Caiming Xiong, Richard Socher
Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play
Sainbayar Sukhbaatar, Zeming Lin, Ilya Kostrikov et al.
i-RevNet: Deep Invertible Networks
Jörn-Henrik Jacobsen, Arnold W.M. Smeulders, Edouard Oyallon
Kernel Implicit Variational Inference
Jiaxin Shi, Shengyang Sun, Jun Zhu
Kronecker-factored Curvature Approximations for Recurrent Neural Networks
James Martens, Jimmy Ba, Matt Johnson
Large scale distributed neural network training through online distillation
Rohan Anil, Gabriel Pereyra, Alexandre Passos et al.
Large Scale Optimal Transport and Mapping Estimation
Vivien Seguy, Bharath Bhushan Damodaran, Remi Flamary et al.
Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models
Jesse Engel, Matthew Hoffman, Adam Roberts
Latent Space Oddity: on the Curvature of Deep Generative Models
Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg
Learning a Generative Model for Validity in Complex Discrete Structures
Dave Janz, Jos van der Westhuizen, Brooks Paige et al.