Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Tanwisuth, Korawat"'
Auto-regressive generation models achieve competitive performance across many different NLP tasks such as summarization, question answering, and classifications. However, they are also known for being slow in inference, which makes them challenging t
Externí odkaz:
http://arxiv.org/abs/2405.04513
Through prompting, large-scale pre-trained models have become more expressive and powerful, gaining significant attention in recent years. Though these big models have zero-shot capabilities, in general, labeled data are still required to adapt them
Externí odkaz:
http://arxiv.org/abs/2305.00350
Unsupervised clustering under domain shift (UCDS) studies how to transfer the knowledge from abundant unlabeled data from multiple source domains to learn the representation of the unlabeled data in a target domain. In this paper, we introduce Protot
Externí odkaz:
http://arxiv.org/abs/2302.03807
Autor:
Wang, Dongsheng, Guo, Dandan, Zhao, He, Zheng, Huangjie, Tanwisuth, Korawat, Chen, Bo, Zhou, Mingyuan
A topic model is often formulated as a generative model that explains how each word of a document is generated given a set of topics and document-specific topic proportions. It is focused on capturing the word co-occurrences in a document and hence o
Externí odkaz:
http://arxiv.org/abs/2203.01570
The neural attention mechanism has been incorporated into deep neural networks to achieve state-of-the-art performance in various domains. Most such models use multi-head self-attention which is appealing for the ability to attend to information from
Externí odkaz:
http://arxiv.org/abs/2110.12567
Autor:
Tanwisuth, Korawat, Fan, Xinjie, Zheng, Huangjie, Zhang, Shujian, Zhang, Hao, Chen, Bo, Zhou, Mingyuan
Existing methods for unsupervised domain adaptation often rely on minimizing some statistical distance between the source and target samples in the latent space. To avoid the sampling variability, class imbalance, and data-privacy concerns that often
Externí odkaz:
http://arxiv.org/abs/2110.12024
Publikováno v:
ICLR 2021
Dropout has been demonstrated as a simple and effective module to not only regularize the training process of deep neural networks, but also provide the uncertainty estimation for prediction. However, the quality of uncertainty estimation is highly d
Externí odkaz:
http://arxiv.org/abs/2103.04181
Deep learning models achieve state-of-the-art performance in many applications but often require large-scale data. Deep transfer learning studies the ability of deep learning models to transfer knowledge from source tasks to related target tasks, ena
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eae7c5c390527811681762d640685bc5