Zobrazeno 1 - 10
of 18 506
pro vyhledávání: '"YU,Fang"'
Publikováno v:
Computer Aided Civil and Infrastructure Engineering, 2016
As the information from diverse disciplines continues to integrate during the whole life cycle of an Architecture, Engineering, and Construction (AEC) project, the BIM (Building Information Model/Modeling) becomes increasingly large. This condition w
Externí odkaz:
http://arxiv.org/abs/2411.09951
Autor:
Chen, Yu-Fang, Chung, Kai-Min, Hsieh, Min-Hsiu, Huang, Wei-Jia, Lengál, Ondřej, Lin, Jyun-Ao, Tsai, Wei-Lun
We present a verifier of quantum programs called AutoQ 2.0. Quantum programs extend quantum circuits (the domain of AutoQ 1.0) by classical control flow constructs, which enable users to describe advanced quantum algorithms in a formal and precise ma
Externí odkaz:
http://arxiv.org/abs/2411.09121
Designing automated market makers (AMMs) for prediction markets on combinatorial securities over large outcome spaces poses significant computational challenges. Prior research has primarily focused on combinatorial prediction markets within specific
Externí odkaz:
http://arxiv.org/abs/2411.08972
Autor:
Huang, Chien-yu, Chen, Wei-Chih, Yang, Shu-wen, Liu, Andy T., Li, Chen-An, Lin, Yu-Xiang, Tseng, Wei-Cheng, Diwan, Anuj, Shih, Yi-Jen, Shi, Jiatong, Chen, William, Chen, Xuanjun, Hsiao, Chi-Yuan, Peng, Puyuan, Wang, Shih-Heng, Kuan, Chun-Yi, Lu, Ke-Han, Chang, Kai-Wei, Yang, Chih-Kai, Ritter-Gutierrez, Fabian, Chuang, Ming To, Huang, Kuan-Po, Arora, Siddhant, Lin, You-Kuan, Yeo, Eunjung, Chang, Kalvin, Chien, Chung-Ming, Choi, Kwanghee, Hsieh, Cheng-Hsiu, Lin, Yi-Cheng, Yu, Chee-En, Chiu, I-Hsiang, Guimarães, Heitor R., Han, Jionghao, Lin, Tzu-Quan, Lin, Tzu-Yuan, Chang, Homu, Chang, Ting-Wu, Chen, Chun Wei, Chen, Shou-Jen, Chen, Yu-Hua, Cheng, Hsi-Chun, Dhawan, Kunal, Fang, Jia-Lin, Fang, Shi-Xin, Chiang, Kuan-Yu Fang, Fu, Chi An, Hsiao, Hsien-Fu, Hsu, Ching Yu, Huang, Shao-Syuan, Wei, Lee Chen, Lin, Hsi-Che, Lin, Hsuan-Hao, Lin, Hsuan-Ting, Lin, Jian-Ren, Liu, Ting-Chun, Lu, Li-Chun, Pai, Tsung-Min, Pasad, Ankita, Kuan, Shih-Yun Shan, Shon, Suwon, Tang, Yuxun, Tsai, Yun-Shao, Wei, Jui-Chiang, Wei, Tzu-Chieh, Wu, Chengxi, Wu, Dien-Ruei, Yang, Chao-Han Huck, Yang, Chieh-Chi, Yip, Jia Qi, Yuan, Shao-Xiang, Noroozi, Vahid, Chen, Zhehuai, Wu, Haibin, Livescu, Karen, Harwath, David, Watanabe, Shinji, Lee, Hung-yi
Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spoken language model that comprehends a wide range of natural language i
Externí odkaz:
http://arxiv.org/abs/2411.05361
Comparison data elicited from people are fundamental to many machine learning tasks, including reinforcement learning from human feedback for large language models and estimating ranking models. They are typically subjective and not directly verifiab
Externí odkaz:
http://arxiv.org/abs/2410.23243
Autor:
Abdulla, Parosh Aziz, Chen, Yo-Ga, Chen, Yu-Fang, Holík, Lukáš, Lengál, Ondřej, Lin, Jyun-Ao, Lo, Fang-Yi, Tsai, Wei-Lun
We present a new method for the verification of quantum circuits based on a novel symbolic representation of sets of quantum states using level-synchronized tree automata (LSTAs). LSTAs extend classical tree automata by labeling each transition with
Externí odkaz:
http://arxiv.org/abs/2410.18540
Autor:
Chang, Chi-Chih, Lin, Wei-Cheng, Lin, Chien-Yu, Chen, Chong-Yan, Hu, Yu-Fang, Wang, Pei-Shuo, Huang, Ning-Chi, Ceze, Luis, Abdelfattah, Mohamed S., Wu, Kai-Chiang
Post-training KV-Cache compression methods typically either sample a subset of effectual tokens or quantize the data into lower numerical bit width. However, these methods cannot exploit redundancy in the hidden dimension of the KV tensors. This pape
Externí odkaz:
http://arxiv.org/abs/2407.21118
High-dimensional single-cell data poses significant challenges in identifying underlying biological patterns due to the complexity and heterogeneity of cellular states. We propose a comprehensive gene-cell dependency visualization via unsupervised cl
Externí odkaz:
http://arxiv.org/abs/2407.16984
In the era of rapid advancements in artificial intelligence (AI), neural network models have achieved notable breakthroughs. However, concerns arise regarding their vulnerability to adversarial attacks. This study focuses on enhancing Concolic Testin
Externí odkaz:
http://arxiv.org/abs/2406.01219
Forecast aggregation combines the predictions of multiple forecasters to improve accuracy. However, the lack of knowledge about forecasters' information structure hinders optimal aggregation. Given a family of information structures, robust forecast
Externí odkaz:
http://arxiv.org/abs/2401.17743