Papers
1,988 papers found
CASTLE: Regularization via Auxiliary Causal Graph Discovery
Trent Kyono, Yao Zhang, Mihaela van der Schaar
Graphon Neural Networks and the Transferability of Graph Neural Networks
Luana Ruiz, Luiz Chamon, Alejandro Ribeiro
Input-Aware Dynamic Backdoor Attack
Tuan Anh Nguyen, Anh Tran
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng et al.
On the Modularity of Hypernetworks
Tomer Galanti, Lior Wolf
GreedyFool: Distortion-Aware Sparse Adversarial Attack
Xiaoyi Dong, Dongdong Chen, Jianmin Bao et al.
Hierarchical Granularity Transfer Learning
Shaobo Min, Hongtao Xie, Hantao Yao et al.
Uncertainty Aware Semi-Supervised Learning on Graph Data
Xujiang Zhao, Feng Chen, Shu Hu et al.
GradAug: A New Regularization Method for Deep Neural Networks
Taojiannan Yang, Sijie Zhu, Chen Chen
Learning Strategy-Aware Linear Classifiers
Yiling Chen, Yang Liu, Chara Podimata
Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity
Shuxiao Chen, Hangfeng He, Weijie Su
OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling
Viet Huynh, He Zhao, Dinh Phung
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
Katja Schwarz, Yiyi Liao, Michael Niemeyer et al.
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
Nicolas Keriven, Alberto Bietti, Samuel Vaiter
CrossTransformers: spatially-aware few-shot transfer
Carl Doersch, Ankush Gupta, Andrew Zisserman
Grounding Representation Similarity Through Statistical Testing
Frances Ding, Jean-Stanislas Denain, Jacob Steinhardt
Cardinality-Regularized Hawkes-Granger Model
Tsuyoshi Ide, Georgios Kollias, Dzung Phan et al.
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning
Jongjin Park, Younggyo Seo, Chang Liu et al.
Contextual Similarity Aggregation with Self-attention for Visual Re-ranking
Jianbo Ouyang, Hui Wu, Min Wang et al.
Learning where to learn: Gradient sparsity in meta and continual learning
Johannes von Oswald, Dominic Zhao, Seijin Kobayashi et al.
A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval
Xiu-Shen Wei, Yang Shen, Xuhao Sun et al.
On the Universality of Graph Neural Networks on Large Random Graphs
Nicolas Keriven, Alberto Bietti, Samuel Vaiter
Weak-shot Fine-grained Classification via Similarity Transfer
Junjie Chen, Li Niu, Liu Liu et al.
SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL
Ruichu Cai, Jinjie Yuan, Boyan Xu et al.
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
Gavin Brown, Marco Gaboardi, Adam Smith et al.