Zobrazeno 1 - 10
of 33 178
pro vyhledávání: '"Fares AS"'
Autor:
Gilson, Aidan, Ai, Xuguang, Arunachalam, Thilaka, Chen, Ziyou, Cheong, Ki Xiong, Dave, Amisha, Duic, Cameron, Kibe, Mercy, Kaminaka, Annette, Prasad, Minali, Siddig, Fares, Singer, Maxwell, Wong, Wendy, Jin, Qiao, Keenan, Tiarnan D. L., Hu, Xia, Chew, Emily Y., Lu, Zhiyong, Xu, Hua, Adelman, Ron A., Tham, Yih-Chung, Chen, Qingyu
Despite the potential of Large Language Models (LLMs) in medicine, they may generate responses lacking supporting evidence or based on hallucinated evidence. While Retrieval Augment Generation (RAG) is popular to address this issue, few studies imple
Externí odkaz:
http://arxiv.org/abs/2409.13902
Predicting traffic flow in data-scarce cities is challenging due to limited historical data. To address this, we leverage transfer learning by identifying periodic patterns common to data-rich cities using a customized variant of Dynamic Mode Decompo
Externí odkaz:
http://arxiv.org/abs/2409.04728
Text recognition in the wild is an important technique for digital maps and urban scene understanding, in which the natural resembling properties between glyphs is one of the major reasons that lead to wrong recognition results. To address this chall
Externí odkaz:
http://arxiv.org/abs/2408.13774
The robustness of neural networks is paramount in safety-critical applications. While most current robustness verification methods assess the worst-case output under the assumption that the input space is known, identifying a verifiable input space $
Externí odkaz:
http://arxiv.org/abs/2408.08824
Transfer learning in reinforcement learning (RL) has become a pivotal strategy for improving data efficiency in new, unseen tasks by utilizing knowledge from previously learned tasks. This approach is especially beneficial in real-world deployment sc
Externí odkaz:
http://arxiv.org/abs/2408.08812
Reinforcement learning (RL) and model predictive control (MPC) each offer distinct advantages and limitations when applied to control problems in power and energy systems. Despite various studies on these methods, benchmarks remain lacking and the pr
Externí odkaz:
http://arxiv.org/abs/2407.15313
We introduce GoldFinch, a hybrid Linear Attention/Transformer sequence model that uses a new technique to efficiently generate a highly compressed and reusable KV-Cache in linear time and space with respect to sequence length. GoldFinch stacks our ne
Externí odkaz:
http://arxiv.org/abs/2407.12077
We study the strong consistency and asymptotic normality of a least squares estimator of the drift coefficient in complex-valued Ornstein-Uhlenbeck processes driven by fractional Brownian motion, extending the results of Chen, Hu, Wang (2017) to the
Externí odkaz:
http://arxiv.org/abs/2406.18004
Autor:
Guerri, Mohamed Fadhlallah, Distante, Cosimo, Spagnolo, Paolo, Bougourzi, Fares, Taleb-Ahmed, Abdelmalik
During the process of classifying Hyperspectral Image (HSI), every pixel sample is categorized under a land-cover type. CNN-based techniques for HSI classification have notably advanced the field by their adept feature representation capabilities. Ho
Externí odkaz:
http://arxiv.org/abs/2406.14120
Autor:
Fares, Samar, Ziu, Klea, Aremu, Toluwani, Durasov, Nikita, Takáč, Martin, Fua, Pascal, Nandakumar, Karthik, Laptev, Ivan
Vision-Language Models (VLMs) are becoming increasingly vulnerable to adversarial attacks as various novel attack strategies are being proposed against these models. While existing defenses excel in unimodal contexts, they currently fall short in saf
Externí odkaz:
http://arxiv.org/abs/2406.09250