Papers
GLSearch: Maximum Common Subgraph Detection via Learning to Search
Yunsheng Bai, Derek Xu, Yizhou Sun et al.
GMAC: A Distributional Perspective on Actor-Critic Framework
Daniel W Nam, Younghoon Kim, Chan Y Park
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings
Matthias Fey, Jan E. Lenssen, Frank Weichert et al.
Goal-Conditioned Reinforcement Learning with Imagined Subgoals
Elliot Chane-Sane, Cordelia Schmid, Ivan Laptev
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
Idan Achituve, Aviv Navon, Yochai Yemini et al.
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix
Maximilian Lam, Gu-Yeon Wei, David Brooks et al.
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training
Krishnateja Killamsetty, Durga S, Ganesh Ramakrishnan et al.
Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech
Vadim Popov, Ivan Vovk, Vladimir Gogoryan et al.
GRAND: Graph Neural Diffusion
Ben Chamberlain, James Rowbottom, Maria I Gorinova et al.
Graph Contrastive Learning Automated
Yuning You, Tianlong Chen, Yang Shen et al.
Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
Aseem Baranwal, Kimon Fountoulakis, Aukosh Jagannath
Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch)
Hunter Lang, David Sontag, Aravindan Vijayaraghavan
GraphDF: A Discrete Flow Model for Molecular Graph Generation
Youzhi Luo, Keqiang Yan, Shuiwang Ji
Graph Mixture Density Networks
Federico Errica, Davide Bacciu, Alessio Micheli
Graph Neural Networks Inspired by Classical Iterative Algorithms
Yongyi Yang, Tang Liu, Yangkun Wang et al.
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
Tianle Cai, Shengjie Luo, Keyulu Xu et al.
Grey-box Extraction of Natural Language Models
Santiago Zanella-Beguelin, Shruti Tople, Andrew Paverd et al.
Grid-Functioned Neural Networks
Javier Dehesa, Andrew Vidler, Julian Padget et al.
Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning
Austin W. Hanjie, Victor Y Zhong, Karthik Narasimhan
Group Fisher Pruning for Practical Network Compression
Liyang Liu, Shilong Zhang, Zhanghui Kuang et al.
Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings
Kan Xu, Xuanyi Zhao, Hamsa Bastani et al.
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
Xiang Wang, Shuai Yuan, Chenwei Wu et al.
Guided Exploration with Proximal Policy Optimization using a Single Demonstration
Gabriele Libardi, Gianni De Fabritiis, Sebastian Dittert
HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search
Niv Nayman, Yonathan Aflalo, Asaf Noy et al.
HAWQ-V3: Dyadic Neural Network Quantization
Zhewei Yao, Zhen Dong, Zhangcheng Zheng et al.