Zobrazeno 1 - 10
of 11 818
pro vyhledávání: '"P Tzeng"'
Large Language Models (LLMs) have received considerable interest in wide applications lately. During pre-training via massive datasets, such a model implicitly memorizes the factual knowledge of trained datasets in its hidden parameters. However, kno
Externí odkaz:
http://arxiv.org/abs/2411.08278
Autor:
Cai, Feiyang, Zhu, Tianyu, Tzeng, Tzuen-Rong, Duan, Yongping, Liu, Ling, Pilla, Srikanth, Li, Gang, Luo, Feng
Artificial intelligence (AI) has significantly advanced computational chemistry research. However, traditional AI methods often rely on task-specific model designs and training, which constrain both the scalability of model size and generalization ac
Externí odkaz:
http://arxiv.org/abs/2410.21422
Autor:
Yang, Pei-Yun, Tzeng, Yu-Chin
Quantum entanglement plays a crucial role not only in understanding Hermitian many-body systems but also in offering valuable insights into non-Hermitian quantum systems. In this paper, we analytically investigate the entanglement Hamiltonian and ent
Externí odkaz:
http://arxiv.org/abs/2409.17062
We propose Exanna, a framework to realize agents that incorporate values in decision making. An Exannaagent considers the values of itself and others when providing rationales for its actions and evaluating the rationales provided by others. Via mult
Externí odkaz:
http://arxiv.org/abs/2408.02117
Publikováno v:
2024 ACM International Conference on Information & Knowledge Management (CIKM)
Adversarial training (AT) can help improve the robustness of Vision Transformers (ViT) against adversarial attacks by intentionally injecting adversarial examples into the training data. However, this way of adversarial injection inevitably incurs st
Externí odkaz:
http://arxiv.org/abs/2407.15385
Autor:
Tzeng, Jing-Tong, Li, Jeng-Lin, Chen, Huan-Yu, Huang, Chun-Hsiang, Chen, Chi-Hsin, Fan, Cheng-Yi, Huang, Edward Pei-Chuan, Lee, Chi-Chun
Deep learning techniques have shown promising results in the automatic classification of respiratory sounds. However, accurately distinguishing these sounds in real-world noisy conditions poses challenges for clinical deployment. Additionally, predic
Externí odkaz:
http://arxiv.org/abs/2407.13895
Federated learning (FL) is an emerging distributed machine learning paradigm that enables collaborative training of machine learning models over decentralized devices without exposing their local data. One of the major challenges in FL is the presenc
Externí odkaz:
http://arxiv.org/abs/2407.07124
Autor:
Wu, Yecun, Xu, Kun, Sarker, Hori Pada, Taniguchi, Takashi, Watanabe, Kenji, Abild-Pedersen, Frank, Majumdar, Arun, Cui, Yi, Tzeng, Yan-Kai, Chu, Steven
Understanding individual ions in solutions is essential for advancing our knowledge of complex chemical systems. However, tracking and detecting ions at the single-ion level in liquid environments remains a challenge. We introduce a strategy for visu
Externí odkaz:
http://arxiv.org/abs/2407.01934
Publikováno v:
KDD 2024 ADS track
Precise crop yield predictions are of national importance for ensuring food security and sustainable agricultural practices. While AI-for-science approaches have exhibited promising achievements in solving many scientific problems such as drug discov
Externí odkaz:
http://arxiv.org/abs/2406.06081
We study the matroid semi-bandits problem, where at each round the learner plays a subset of $K$ arms from a feasible set, and the goal is to maximize the expected cumulative linear rewards. Existing algorithms have per-round time complexity at least
Externí odkaz:
http://arxiv.org/abs/2405.17968