463 citations · 5.1k across the 190 of their papers we have counts for
39 papers · 1 filter
Learning Graphons via Structured Gromov-Wasserstein Barycenters
Hongteng Xu, Dixin Luo, Lawrence Carin +1
We propose a novel and principled method to learn a nonparametric graph model called graphon, which is defined in an infinite-dimensional space and represents arbitrary-size graphs…
Wasserstein Contrastive Representation Distillation
Liqun Chen, Dong Wang, Zhe Gan +3
The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student network, with the latter being more compa…
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer
Zidi Xiu, Junya Chen, Ricardo Henao +3
Dealing with severe class imbalance poses a major challenge for real-world applications, especially when the accurate classification and generalization of minority classes is of pr…
Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks
Rachel Lea Draelos, Lawrence Carin
Explanation methods facilitate the development of models that learn meaningful concepts and avoid exploiting spurious correlations. We illustrate a previously unrecognized limitati…
MixKD: Towards Efficient Distillation of Large-scale Language Models
Kevin J Liang, Weituo Hao, Dinghan Shen +4
Large-scale language models have recently demonstrated impressive empirical performance. Nevertheless, the improved results are attained at the price of bigger models, more power c…
Double Robust Representation Learning for Counterfactual Prediction
Shuxi Zeng, Serge Assaad, Chenyang Tao +3
Causal inference, or counterfactual prediction, is central to decision making in healthcare, policy and social sciences. To de-bias causal estimators with high-dimensional data in…