Zobrazeno 1 - 10
of 36 776
pro vyhledávání: '"Chang, Yu"'
The paper introduces a Fuzzy-based Attention (Fuzzy Attention Layer) mechanism, a novel computational approach to enhance the interpretability and efficacy of neural models in psychological research. The proposed Fuzzy Attention Layer mechanism is in
Externí odkaz:
http://arxiv.org/abs/2409.17661
Autor:
Satrya, Christoforus Dimas, Chang, Yu-Cheng, Upadhyay, Rishabh, Makinen, Ilari K., Peltonen, Joonas T., Karimi, Bayan, Pekola, Jukka P.
Superconducting circuits provide a versatile and controllable platform for studies of fundamental quantum phenomena as well as for quantum technology applications. A conventional technique to read out the state of a quantum circuit or to characterize
Externí odkaz:
http://arxiv.org/abs/2409.13417
In the realm of drug discovery, DNA-encoded library (DEL) screening technology has emerged as an efficient method for identifying high-affinity compounds. However, DEL screening faces a significant challenge: noise arising from nonspecific interactio
Externí odkaz:
http://arxiv.org/abs/2409.05916
This paper presents a pioneering exploration into the integration of fine-grained human supervision within the autonomous driving domain to enhance system performance. The current advances in End-to-End autonomous driving normally are data-driven and
Externí odkaz:
http://arxiv.org/abs/2408.10908
Driving under drowsy conditions significantly escalates the risk of vehicular accidents. Although recent efforts have focused on using electroencephalography to detect drowsiness, helping prevent accidents caused by driving in such states, seamless h
Externí odkaz:
http://arxiv.org/abs/2408.07083
Autor:
Zhou, Jinzhao, Duan, Yiqun, Zhao, Ziyi, Chang, Yu-Cheng, Wang, Yu-Kai, Do, Thomas, Lin, Chin-Teng
Decoding linguistic information from non-invasive brain signals using EEG has gained increasing research attention due to its vast applicational potential. Recently, a number of works have adopted a generative-based framework to decode electroencepha
Externí odkaz:
http://arxiv.org/abs/2408.04679
Autor:
Yin, Mingze, Zhou, Hanjing, Zhu, Yiheng, Lin, Miao, Wu, Yixuan, Wu, Jialu, Xu, Hongxia, Hsieh, Chang-Yu, Hou, Tingjun, Chen, Jintai, Wu, Jian
Proteins govern most biological functions essential for life, but achieving controllable protein discovery and optimization remains challenging. Recently, machine learning-assisted protein editing (MLPE) has shown promise in accelerating optimization
Externí odkaz:
http://arxiv.org/abs/2407.19296
Autor:
Wang, Jike, Feng, Jianwen, Kang, Yu, Pan, Peichen, Ge, Jingxuan, Wang, Yan, Wang, Mingyang, Wu, Zhenxing, Zhang, Xingcai, Yu, Jiameng, Zhang, Xujun, Wang, Tianyue, Wen, Lirong, Yan, Guangning, Deng, Yafeng, Shi, Hui, Hsieh, Chang-Yu, Jiang, Zhihui, Hou, Tingjun
We propose AMP-Designer, an LLM-based foundation model approach for the rapid design of novel antimicrobial peptides (AMPs) with multiple desired properties. Within 11 days, AMP-Designer enables de novo design of 18 novel candidates with broad-spectr
Externí odkaz:
http://arxiv.org/abs/2407.12296
Autor:
Wang, Jike, Qin, Rui, Wang, Mingyang, Fang, Meijing, Zhang, Yangyang, Zhu, Yuchen, Su, Qun, Gou, Qiaolin, Shen, Chao, Zhang, Odin, Wu, Zhenxing, Jiang, Dejun, Zhang, Xujun, Zhao, Huifeng, Wan, Xiaozhe, Wu, Zhourui, Liu, Liwei, Kang, Yu, Hsieh, Chang-Yu, Hou, Tingjun
Significant interests have recently risen in leveraging sequence-based large language models (LLMs) for drug design. However, most current applications of LLMs in drug discovery lack the ability to comprehend three-dimensional (3D) structures, thereb
Externí odkaz:
http://arxiv.org/abs/2407.07930
Ensembling multiple models has always been an effective approach to push the limits of existing performance and is widely used in classification tasks by simply averaging the classification probability vectors from multiple classifiers to achieve bet
Externí odkaz:
http://arxiv.org/abs/2406.12585