Papers
11,951 papers found
Generative Models for Effective ML on Private, Decentralized Datasets
Sean Augenstein, H. Brendan McMahan, Daniel Ramage et al.
Generative Ratio Matching Networks
Akash Srivastava, Kai Xu, Michael U. Gutmann et al.
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
Martin Engelcke, Adam R. Kosiorek, Oiwi Parker Jones et al.
Geometric Analysis of Nonconvex Optimization Landscapes for Overcomplete Learning
Qing Qu, Yuexiang Zhai, Xiao Li et al.
Geometric Insights into the Convergence of Nonlinear TD Learning
David Brandfonbrener, Joan Bruna
Geom-GCN: Geometric Graph Convolutional Networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang et al.
Geomstats: A Python Package for Riemannian Geometry in Machine Learning
Nina Miolane, Nicolas Guigui, Alice Le Brigant et al.
GLAD: Learning Sparse Graph Recovery
Harsh Shrivastava, Xinshi Chen, Binghong Chen et al.
Global Relational Models of Source Code
Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh et al.
Gradient $\ell_1$ Regularization for Quantization Robustness
Milad Alizadeh, Arash Behboodi, Mart van Baalen et al.
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.