6 citations · 6 across the 6 of their papers we have counts for
6 papers
Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation
Jiajin Zhang, Hanqing Chao, Amit Dhurandhar +4
Domain shift is a common problem in clinical applications, where the training images (source domain) and the test images (target domain) are under different distributions. Unsuperv…
Decentralized gradient descent maximization method for composite nonconvex strongly-concave minimax problems
Yangyang Xu
Minimax problems have recently attracted a lot of research interests. A few efforts have been made to solve decentralized nonconvex strongly-concave (NCSC) minimax-structured optim…
DeMT: Deformable Mixer Transformer for Multi-Task Learning of Dense Prediction
Yangyang Xu, Yibo Yang, Lefei Zhang
Convolution neural networks (CNNs) and Transformers have their own advantages and both have been widely used for dense prediction in multi-task learning (MTL). Most of the current…
Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data
Yonggui Yan, Jie Chen, Pin-Yu Chen +3
We first propose a decentralized proximal stochastic gradient tracking method (DProxSGT) for nonconvex stochastic composite problems, with data heterogeneously distributed on multi…
Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis
Jia Li, Ziyang Zhang, Junjie Lang +9
In this paper, we present our solutions for the Multimodal Sentiment Analysis Challenge (MuSe) 2022, which includes MuSe-Humor, MuSe-Reaction and MuSe-Stress Sub-challenges. The Mu…
Zeroth-order Optimization for Composite Problems with Functional Constraints
Zichong Li, Pin-Yu Chen, Sijia Liu +2
In many real-world problems, first-order (FO) derivative evaluations are too expensive or even inaccessible. For solving these problems, zeroth-order (ZO) methods that only need fu…