2.6k citations · 4.6k across the 107 of their papers we have counts for
10 papers · 1 filter
Steering Prototypes with Prompt-tuning for Rehearsal-free Continual Learning
Zhuowei Li, Long Zhao, Zizhao Zhang +4
In the context of continual learning, prototypes-as representative class embeddings-offer advantages in memory conservation and the mitigation of catastrophic forgetting. However,…
A Manifold View of Adversarial Risk
Wenjia Zhang, Yikai Zhang, Xiaoling Hu +3
The adversarial risk of a machine learning model has been widely studied. Most previous works assume that the data lies in the whole ambient space. We propose to take a new angle a…
Global and Local Interpretation of black-box Machine Learning models to determine prognostic factors from early COVID-19 data
Ananya Jana, Carlos D. Minacapelli, Vinod Rustgi +1
The COVID-19 corona virus has claimed 4.1 million lives, as of July 24, 2021. A variety of machine learning models have been applied to related data to predict important factors su…
Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information
Qi Chang, Zhennan Yan, Lohendran Baskaran +5
As deep learning technologies advance, increasingly more data is necessary to generate general and robust models for various tasks. In the medical domain, however, large-scale and…
Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons
Ligong Han, Ruijiang Gao, Mun Kim +3
Conditional generative adversarial networks have shown exceptional generation performance over the past few years. However, they require large numbers of annotations. To address th…
Rethinking Kernel Methods for Node Representation Learning on Graphs
Yu Tian, Long Zhao, Xi Peng +1
Graph kernels are kernel methods measuring graph similarity and serve as a standard tool for graph classification. However, the use of kernel methods for node classification, which…
Greedy AutoAugment
Alireza Naghizadeh, Mohammadsajad Abavisani, Dimitris N. Metaxas
A major problem in data augmentation is to ensure that the generated new samples cover the search space. This is a challenging problem and requires exploration for data augmentatio…
Unsupervised Domain Adaptation via Calibrating Uncertainties
Ligong Han, Yang Zou, Ruijiang Gao +2
Unsupervised domain adaptation (UDA) aims at inferring class labels for unlabeled target domain given a related labeled source dataset. Intuitively, a model trained on source domai…
Improving GANs Using Optimal Transport
Tim Salimans, Han Zhang, Alec Radford +1
We present Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data d…
Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximization
Bo Liu, Xiao-Tong Yuan, Lezi Wang +2
Iterative Hard Thresholding (IHT) is a class of projected gradient descent methods for optimizing sparsity-constrained minimization models, with the best known efficiency and scala…