Papers
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
Mingchao Yu, Zhifeng Lin, Krishna Narra et al.
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee et al.
Gradually Updated Neural Networks for Large-Scale Image Recognition
Siyuan Qiao, Zhishuai Zhang, Wei Shen et al.
Grammar Induction with Neural Language Models: An Unusual Replication
Phu Mon Htut, Kyunghyun Cho, Samuel Bowman
Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova et al.
Graph Based Decoding for Event Sequencing and Coreference Resolution
Zhengzhong Liu, Teruko Mitamura, Eduard Hovy
Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models
Satoru Katsumata, Yukio Matsumura, Hayahide Yamagishi et al.
GraphBit: Bitwise Interaction Mining via Deep Reinforcement Learning
Yueqi Duan, Ziwei Wang, Jiwen Lu et al.
GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model
Qile Zhu, Zheng Feng, Xiaolin Li
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Jiaxuan You, Bowen Liu, Zhitao Ying et al.
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
Yuhao Zhang, Peng Qi, Christopher D. Manning
Graph-Cut RANSAC
Daniel Barath, Jiří Matas
Graphene: a Context-Preserving Open Information Extraction System
Matthias Cetto, Christina Niklaus, André Freitas et al.
Graphene: Semantically-Linked Propositions in Open Information Extraction
Matthias Cetto, Christina Niklaus, André Freitas et al.
Graphical Generative Adversarial Networks
Chongxuan LI, Max Welling, Jun Zhu et al.
Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Fredrik Lindsten, Jouni Helske, Matti Vihola
Graphical Models for Non-Negative Data Using Generalized Score Matching
Shiqing Yu, Mathias Drton, Ali Shojaie
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.
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
Blake E Woodworth, Jialei Wang, Adam Smith et al.
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Jiaxuan You, Rex Ying, Xiang Ren et al.
Graph-to-Sequence Learning using Gated Graph Neural Networks
Daniel Beck, Gholamreza Haffari, Trevor Cohn
Grasp2Vec: Learning Object Representations from Self-Supervised Grasping
Eric Jang, Coline Devin, Vincent Vanhoucke et al.
GraspNet: An Efficient Convolutional Neural Network for Real-time Grasp Detection for Low-powered Devices
Umar Asif, Jianbin Tang, Stefan Harrer
Graviton: Trusted Execution Environments on GPUs
Stavros Volos, Kapil Vaswani, Rodrigo Bruno