254 citations · 304 across the 20 of their papers we have counts for
5 papers · 1 filter
Towards Effective Semantic OOD Detection in Unseen Domains: A Domain Generalization Perspective
Haoliang Wang, Chen Zhao, Yunhui Guo +2
Two prevalent types of distributional shifts in machine learning are the covariate shift (as observed across different domains) and the semantic shift (as seen across different cla…
Towards Fair Disentangled Online Learning for Changing Environments
Chen Zhao, Feng Mi, Xintao Wu +4
In the problem of online learning for changing environments, data are sequentially received one after another over time, and their distribution assumptions may vary frequently. Alt…
Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
Aneesh Komanduri, Yongkai Wu, Feng Chen +1
Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information f…
InterFormer: Interactive Local and Global Features Fusion for Automatic Speech Recognition
Zhi-Hao Lai, Tian-Hao Zhang, Qi Liu +5
The local and global features are both essential for automatic speech recognition (ASR). Many recent methods have verified that simply combining local and global features can furth…
Rethinking Speech Recognition with A Multimodal Perspective via Acoustic and Semantic Cooperative Decoding
Tian-Hao Zhang, Hai-Bo Qin, Zhi-Hao Lai +5
Attention-based encoder-decoder (AED) models have shown impressive performance in ASR. However, most existing AED methods neglect to simultaneously leverage both acoustic and seman…