20 citations · 53 across the 7 of their papers we have counts for
7 papers
Recovering from Privacy-Preserving Masking with Large Language Models
Arpita Vats, Zhe Liu, Peng Su +5
Model adaptation is crucial to handle the discrepancy between proxy training data and actual users data received. To effectively perform adaptation, textual data of users is typica…
Language Agnostic Data-Driven Inverse Text Normalization
Szu-Jui Chen, Debjyoti Paul, Yutong Pang +2
With the emergence of automatic speech recognition (ASR) models, converting the spoken form text (from ASR) to the written form is in urgent need. This inverse text normalization (…
Gradient Regularized Contrastive Learning for Continual Domain Adaptation
Shixiang Tang, Peng Su, Dapeng Chen +1
Human beings can quickly adapt to environmental changes by leveraging learning experience. However, adapting deep neural networks to dynamic environments by machine learning algori…
Contrastive Visual-Linguistic Pretraining
Lei Shi, Kai Shuang, Shijie Geng +6
Several multi-modality representation learning approaches such as LXMERT and ViLBERT have been proposed recently. Such approaches can achieve superior performance due to the high-l…
Gradient Regularized Contrastive Learning for Continual Domain Adaptation
Peng Su, Shixiang Tang, Peng Gao +3
Human beings can quickly adapt to environmental changes by leveraging learning experience. However, the poor ability of adapting to dynamic environments remains a major challenge f…
Modal Uncertainty Estimation via Discrete Latent Representation
Di Qiu, Lok Ming Lui
Many important problems in the real world don't have unique solutions. It is thus important for machine learning models to be capable of proposing different plausible solutions wit…