Papers
11,015 papers found
Gradient-Based Neural DAG Learning
Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu et al.
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks
Kaifeng Lyu, Jian Li
Gradientless Descent: High-Dimensional Zeroth-Order Optimization
Daniel Golovin, John Karro, Greg Kochanski et al.
Gradients as Features for Deep Representation Learning
Fangzhou Mu, Yingyu Liang, Yin Li
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Chence Shi*, Minkai Xu*, Zhaocheng Zhu et al.
Graph Constrained Reinforcement Learning for Natural Language Action Spaces
Prithviraj Ammanabrolu, Matthew Hausknecht
Graph Convolutional Reinforcement Learning
Jiechuan Jiang, Chen Dun, Tiejun Huang et al.
Graph inference learning for semi-supervised classification
Chunyan Xu, Zhen Cui, Xiaobin Hong et al.
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
Kenta Oono, Taiji Suzuki
GraphSAINT: Graph Sampling Based Inductive Learning Method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava et al.
GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding
Chenhui Deng, Zhiqiang Zhao, Yongyu Wang et al.
Guiding Program Synthesis by Learning to Generate Examples
Larissa Laich, Pavol Bielik, Martin Vechev
Hamiltonian Generative Networks
Peter Toth, Danilo J. Rezende, Andrew Jaegle et al.
Harnessing Structures for Value-Based Planning and Reinforcement Learning
Yuzhe Yang, Guo Zhang, Zhi Xu et al.
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
Sanjeev Arora, Simon S. Du, Zhiyuan Li et al.
Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation
Suraj Nair, Chelsea Finn
Higher-Order Function Networks for Learning Composable 3D Object Representations
Eric Mitchell, Selim Engin, Volkan Isler et al.
High Fidelity Speech Synthesis with Adversarial Networks
Mikołaj Bińkowski, Jeff Donahue, Sander Dieleman et al.
HiLLoC: lossless image compression with hierarchical latent variable models
James Townsend, Thomas Bird, Julius Kunze et al.
HOPPITY: LEARNING GRAPH TRANSFORMATIONS TO DETECT AND FIX BUGS IN PROGRAMS
Elizabeth Dinella, Hanjun Dai, Ziyang Li et al.
How much Position Information Do Convolutional Neural Networks Encode?
Md Amirul Islam*, Sen Jia*, Neil D. B. Bruce
How to 0wn the NAS in Your Spare Time
Sanghyun Hong, Michael Davinroy, Yiǧitcan Kaya et al.
Hypermodels for Exploration
Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi et al.
Hyper-SAGNN: a self-attention based graph neural network for hypergraphs
Ruochi Zhang, Yuesong Zou, Jian Ma
I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively
Haotao Wang, Tianlong Chen, Zhangyang Wang et al.