Papers
8,340 papers found
Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models
Nikhil Kandpal, Brian Lester, Mohammed Muqeeth et al.
Global Context Vision Transformers
Ali Hatamizadeh, Hongxu Yin, Greg Heinrich et al.
Global optimality for Euclidean CCCP under Riemannian convexity
Melanie Weber, Suvrit Sra
Global optimality of Elman-type RNNs in the mean-field regime
Andrea Agazzi, Jianfeng Lu, Sayan Mukherjee
Global Optimization with Parametric Function Approximation
Chong Liu, Yu-Xiang Wang
Global Selection of Contrastive Batches via Optimization on Sample Permutations
Vin Sachidananda, Ziyi Yang, Chenguang Zhu
GLOBE-CE: A Translation Based Approach for Global Counterfactual Explanations
Dan Ley, Saumitra Mishra, Daniele Magazzeni
GNN&GBDT-Guided Fast Optimizing Framework for Large-scale Integer Programming
Huigen Ye, Hua Xu, Hongyan Wang et al.
GNOT: A General Neural Operator Transformer for Operator Learning
Zhongkai Hao, Zhengyi Wang, Hang Su et al.
GOAT: A Global Transformer on Large-scale Graphs
Kezhi Kong, Jiuhai Chen, John Kirchenbauer et al.
Go Beyond Imagination: Maximizing Episodic Reachability with World Models
Yao Fu, Run Peng, Honglak Lee
Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space
Weitang Liu, Yi-Zhuang You, Ying Wai Li et al.
Gradient Descent Converges Linearly for Logistic Regression on Separable Data
Kyriakos Axiotis, Maxim Sviridenko
Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks
Yihang Gao, Yiqi Gu, Michael Ng
Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space
Alistair Shilton, Sunil Gupta, Santu Rana et al.
Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond
Itai Kreisler, Mor Shpigel Nacson, Daniel Soudry et al.
Gradient-Free Structured Pruning with Unlabeled Data
Azade Nova, Hanjun Dai, Dale Schuurmans
GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets
Shubham Gupta, Sahil Manchanda, Sayan Ranu et al.
GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks
Yuwen Li, Miao Xiong, Bryan Hooi
Graph Contrastive Backdoor Attacks
Hangfan Zhang, Jinghui Chen, Lu Lin et al.
Graph Generative Model for Benchmarking Graph Neural Networks
Minji Yoon, Yue Wu, John Palowitch et al.
Graphically Structured Diffusion Models
Christian Dietrich Weilbach, William Harvey, Frank Wood
Graph Inductive Biases in Transformers without Message Passing
Liheng Ma, Chen Lin, Derek Lim et al.
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication
Ajay Kumar Jaiswal, Shiwei Liu, Tianlong Chen et al.
Graph Mixup with Soft Alignments
Hongyi Ling, Zhimeng Jiang, Meng Liu et al.