no code implementations • 7 May 2024 • Shujian Zhang, Korawat Tanwisuth, Chengyue Gong, Pengcheng He, Mingyuan Zhou
Auto-regressive generation models achieve competitive performance across many different NLP tasks such as summarization, question answering, and classifications.
1 code implementation • 29 Apr 2023 • Korawat Tanwisuth, Shujian Zhang, Huangjie Zheng, Pengcheng He, Mingyuan Zhou
Through prompting, large-scale pre-trained models have become more expressive and powerful, gaining significant attention in recent years.
no code implementations • 8 Feb 2023 • Korawat Tanwisuth, Shujian Zhang, Pengcheng He, Mingyuan Zhou
Finally, it refines the target model on the target domain data without guidance from the source model.
2 code implementations • ICLR 2022 • Dongsheng Wang, Dandan Guo, He Zhao, Huangjie Zheng, Korawat Tanwisuth, Bo Chen, Mingyuan Zhou
This paper introduces a new topic-modeling framework where each document is viewed as a set of word embedding vectors and each topic is modeled as an embedding vector in the same embedding space.
1 code implementation • NeurIPS 2021 • Shujian Zhang, Xinjie Fan, Huangjie Zheng, Korawat Tanwisuth, Mingyuan Zhou
The neural attention mechanism has been incorporated into deep neural networks to achieve state-of-the-art performance in various domains.
1 code implementation • NeurIPS 2021 • Korawat Tanwisuth, Xinjie Fan, Huangjie Zheng, Shujian Zhang, Hao Zhang, Bo Chen, Mingyuan Zhou
Existing methods for unsupervised domain adaptation often rely on minimizing some statistical distance between the source and target samples in the latent space.
1 code implementation • ICLR 2021 • Xinjie Fan, Shujian Zhang, Korawat Tanwisuth, Xiaoning Qian, Mingyuan Zhou
However, the quality of uncertainty estimation is highly dependent on the dropout probabilities.