Papers
11,955 papers found
Generative Modeling Helps Weak Supervision (and Vice Versa)
Benedikt Boecking, Nicholas Roberts, Willie Neiswanger et al.
Generative Modelling with Inverse Heat Dissipation
Severi Rissanen, Markus Heinonen, Arno Solin
Geometrically regularized autoencoders for non-Euclidean data
Cheongjae Jang, Yonghyeon Lee, Yung-Kyun Noh et al.
GFlowNets and variational inference
Nikolay Malkin, Salem Lahlou, Tristan Deleu et al.
Git Re-Basin: Merging Models modulo Permutation Symmetries
Samuel Ainsworth, Jonathan Hayase, Siddhartha Srinivasa
GLM-130B: An Open Bilingual Pre-trained Model
Aohan Zeng, Xiao Liu, Zhengxiao Du et al.
Global Explainability of GNNs via Logic Combination of Learned Concepts
Steve Azzolin, Antonio Longa, Pietro Barbiero et al.
Global Identifiability of $\ell_1$-based Dictionary Learning via Matrix Volume Optimization
Jingzhou Hu, Kejun Huang
Globally Optimal Training of Neural Networks with Threshold Activation Functions
Tolga Ergen, Halil Ibrahim Gulluk, Jonathan Lacotte et al.
GNNDelete: A General Strategy for Unlearning in Graph Neural Networks
Jiali Cheng, George Dasoulas, Huan He et al.
GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
Xiaoqi Wang, Han Wei Shen
GoBigger: A Scalable Platform for Cooperative-Competitive Multi-Agent Interactive Simulation
Ming Zhang, Shenghan Zhang, Zhenjie Yang et al.
GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure
Tennison Liu, Zhaozhi Qian, Jeroen Berrevoets et al.
GOOD: Exploring geometric cues for detecting objects in an open world
Haiwen Huang, Andreas Geiger, Dan Zhang
GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation
Chenhongyi Yang, Jiarui Xu, Shalini De Mello et al.
GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints
Mohammadsajad Abavisani, David Danks, Sergey Plis
Gradient Boosting Performs Gaussian Process Inference
Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova
Gradient Gating for Deep Multi-Rate Learning on Graphs
T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney et al.
Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models
Meng Liu, Haoran Liu, Shuiwang Ji
Grammar Prompting for Domain-Specific Language Generation with Large Language Models
Bailin Wang, Zi Wang, Xuezhi Wang et al.
Graph-based Deterministic Policy Gradient for Repetitive Combinatorial Optimization Problems
Zhongyuan Zhao, Ananthram Swami, Santiago Segarra
Graph Contrastive Learning for Skeleton-based Action Recognition
Xiaohu Huang, Hao Zhou, Jian Wang et al.
Graph Domain Adaptation via Theory-Grounded Spectral Regularization
Yuning You, Tianlong Chen, Zhangyang Wang et al.
Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
Zehao Niu, Mihai Anitescu, Jie Chen
Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs
Chenxiao Yang, Qitian Wu, Jiahua Wang et al.