Zobrazeno 1 - 10
of 3 969
pro vyhledávání: '"Thanh Tung On"'
Autor:
Dwivedi, Vijay Prakash, Schlegel, Viktor, Liu, Andy T., Nguyen, Thanh-Tung, Kashyap, Abhinav Ramesh, Wei, Jeng, Yin, Wei-Hsian, Winkler, Stefan, Tan, Robby T.
Large Language Models (LLMs) have demonstrated remarkable performance across various domains, including healthcare. However, their ability to effectively represent structured non-textual data, such as the alphanumeric medical codes used in records li
Externí odkaz:
http://arxiv.org/abs/2410.13351
Biopharmaceutical products, particularly monoclonal antibodies (mAbs), have gained prominence in the pharmaceutical market due to their high specificity and efficacy. As these products are projected to constitute a substantial portion of global pharm
Externí odkaz:
http://arxiv.org/abs/2409.02149
Autor:
Binici, Kuluhan, Kashyap, Abhinav Ramesh, Schlegel, Viktor, Liu, Andy T., Dwivedi, Vijay Prakash, Nguyen, Thanh-Tung, Gao, Xiaoxue, Chen, Nancy F., Winkler, Stefan
Automatic Speech Recognition (ASR) systems are pivotal in transcribing speech into text, yet the errors they introduce can significantly degrade the performance of downstream tasks like summarization. This issue is particularly pronounced in clinical
Externí odkaz:
http://arxiv.org/abs/2408.14418
Autor:
Nagar, Aishik, Schlegel, Viktor, Nguyen, Thanh-Tung, Li, Hao, Wu, Yuping, Binici, Kuluhan, Winkler, Stefan
Large Language Models (LLMs) are increasingly adopted for applications in healthcare, reaching the performance of domain experts on tasks such as question answering and document summarisation. Despite their success on these tasks, it is unclear how w
Externí odkaz:
http://arxiv.org/abs/2408.12249
Representation Misdirection for Unlearning (RMU), which steers model representation in the intermediate layer to a target random representation, is an effective method for large language model (LLM) unlearning. Despite its high performance, the under
Externí odkaz:
http://arxiv.org/abs/2408.06223
Autor:
Nguyen, Quang H., Ngoc-Hieu, Nguyen, Ta, The-Anh, Nguyen-Tang, Thanh, Wong, Kok-Seng, Thanh-Tung, Hoang, Doan, Khoa D.
Deep neural networks are vulnerable to backdoor attacks, a type of adversarial attack that poisons the training data to manipulate the behavior of models trained on such data. Clean-label attacks are a more stealthy form of backdoor attacks that can
Externí odkaz:
http://arxiv.org/abs/2407.10825
Autor:
Subramanian, Anand, Schlegel, Viktor, Kashyap, Abhinav Ramesh, Nguyen, Thanh-Tung, Dwivedi, Vijay Prakash, Winkler, Stefan
There is vivid research on adapting Large Language Models (LLMs) to perform a variety of tasks in high-stakes domains such as healthcare. Despite their popularity, there is a lack of understanding of the extent and contributing factors that allow LLM
Externí odkaz:
http://arxiv.org/abs/2406.03699
Autor:
Schlegel, Viktor, Kashyap, Abhinav Ramesh, Nguyen, Thanh-Tung, Yang, Tsung-Han, Dwivedi, Vijay Prakash, Yin, Wei-Hsian, Wei, Jeng, Winkler, Stefan
Computerised clinical coding approaches aim to automate the process of assigning a set of codes to medical records. While there is active research pushing the state of the art on clinical coding for hospitalized patients, the outpatient setting -- wh
Externí odkaz:
http://arxiv.org/abs/2312.13533
Denoising Probabilistic Models (DPMs) represent an emerging domain of generative models that excel in generating diverse and high-quality images. However, most current training methods for DPMs often neglect the correlation between timesteps, limitin
Externí odkaz:
http://arxiv.org/abs/2312.12431
While machine learning (ML) has made significant contributions to the biopharmaceutical field, its applications are still in the early stages in terms of providing direct support for quality-by-design based development and manufacturing of biopharmac
Externí odkaz:
http://arxiv.org/abs/2310.09991