Zobrazeno 1 - 10
of 4 844
pro vyhledávání: '"YAO Jing"'
We study optimal payoff choice for an expected utility maximizer under the constraint that their payoff is not allowed to deviate ``too much'' from a given benchmark. We solve this problem when the deviation is assessed via a Bregman-Wasserstein (BW)
Externí odkaz:
http://arxiv.org/abs/2411.18397
Recently, road graph extraction has garnered increasing attention due to its crucial role in autonomous driving, navigation, etc. However, accurately and efficiently extracting road graphs remains a persistent challenge, primarily due to the severe s
Externí odkaz:
http://arxiv.org/abs/2411.16733
Remote sensing image object detection (RSIOD) aims to identify and locate specific objects within satellite or aerial imagery. However, there is a scarcity of labeled data in current RSIOD datasets, which significantly limits the performance of curre
Externí odkaz:
http://arxiv.org/abs/2411.15497
Autor:
Lin, Fangru, Mao, Shaoguang, La Malfa, Emanuele, Hofmann, Valentin, de Wynter, Adrian, Yao, Jing, Chen, Si-Qing, Wooldridge, Michael, Wei, Furu
Language is not monolithic. While many benchmarks are used as proxies to systematically estimate Large Language Models' (LLM) performance in real-life tasks, they tend to ignore the nuances of within-language variation and thus fail to model the expe
Externí odkaz:
http://arxiv.org/abs/2410.11005
Autor:
Liu, Yan, Yi, Xiaoyuan, Chen, Xiaokang, Yao, Jing, Yi, Jingwei, Zan, Daoguang, Liu, Zheng, Xie, Xing, Ho, Tsung-Yi
The demand for regulating potentially risky behaviors of large language models (LLMs) has ignited research on alignment methods. Since LLM alignment heavily relies on reward models for optimization or evaluation, neglecting the quality of reward mode
Externí odkaz:
http://arxiv.org/abs/2409.19024
The rapid progress in Large Language Models (LLMs) poses potential risks such as generating unethical content. Assessing LLMs' values can help expose their misalignment, but relies on reference-free evaluators, e.g., fine-tuned LLMs or close-source o
Externí odkaz:
http://arxiv.org/abs/2407.10725
Recurrent neural networks and Transformers have recently dominated most applications in hyperspectral (HS) imaging, owing to their capability to capture long-range dependencies from spectrum sequences. However, despite the success of these sequential
Externí odkaz:
http://arxiv.org/abs/2404.08489
Self-supervised multi-frame methods have currently achieved promising results in depth estimation. However, these methods often suffer from mismatch problems due to the moving objects, which break the static assumption. Additionally, unfairness can o
Externí odkaz:
http://arxiv.org/abs/2403.19294
This paper introduces RecAI, a practical toolkit designed to augment or even revolutionize recommender systems with the advanced capabilities of Large Language Models (LLMs). RecAI provides a suite of tools, including Recommender AI Agent, Recommenda
Externí odkaz:
http://arxiv.org/abs/2403.06465
Autor:
Wang, Xinpeng, Duan, Shitong, Yi, Xiaoyuan, Yao, Jing, Zhou, Shanlin, Wei, Zhihua, Zhang, Peng, Xu, Dongkuan, Sun, Maosong, Xie, Xing
Big models have achieved revolutionary breakthroughs in the field of AI, but they might also pose potential concerns. Addressing such concerns, alignment technologies were introduced to make these models conform to human preferences and values. Despi
Externí odkaz:
http://arxiv.org/abs/2403.04204