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pro vyhledávání: '"CHEN, Feng"'
Autor:
Chen, Feng, Gou, Chenhui, Liu, Jing, Yang, Yang, Li, Zhaoyang, Zhang, Jiyuan, Sun, Zhenbang, Zhuang, Bohan, Wu, Qi
As multimodal large language models (MLLMs) advance rapidly, rigorous evaluation has become essential, providing further guidance for their development. In this work, we focus on a unified and robust evaluation of \textbf{vision perception} abilities
Externí odkaz:
http://arxiv.org/abs/2411.14725
Given the inherent non-stationarity prevalent in real-world applications, continual Reinforcement Learning (RL) aims to equip the agent with the capability to address a series of sequentially presented decision-making tasks. Within this problem setti
Externí odkaz:
http://arxiv.org/abs/2411.10809
Open-set domain generalization addresses a real-world challenge: training a model to generalize across unseen domains (domain generalization) while also detecting samples from unknown classes not encountered during training (open-set recognition). Ho
Externí odkaz:
http://arxiv.org/abs/2411.07392
Autor:
Zhang, Zeyu, Gao, Hang, Liu, Akide, Chen, Qi, Chen, Feng, Wang, Yiran, Li, Danning, Tang, Hao
Human motion generation is a cut-edge area of research in generative computer vision, with promising applications in video creation, game development, and robotic manipulation. The recent Mamba architecture shows promising results in efficiently mode
Externí odkaz:
http://arxiv.org/abs/2411.06481
As a core component in modern data centers, key-value cache provides high-throughput and low-latency services for high-speed data processing. The effectiveness of a key-value cache relies on its ability of accommodating the needed data. However, expa
Externí odkaz:
http://arxiv.org/abs/2411.03174
The emerging in-context learning (ICL) ability of large language models (LLMs) has prompted their use for predictive tasks in various domains with different types of data facilitated by serialization methods. However, with increasing applications in
Externí odkaz:
http://arxiv.org/abs/2411.02671
Real-world machine learning applications often face simultaneous covariate and semantic shifts, challenging traditional domain generalization and out-of-distribution (OOD) detection methods. We introduce Meta-learned Across Domain Out-of-distribution
Externí odkaz:
http://arxiv.org/abs/2411.02444
Generalizing to out-of-distribution data while being aware of model fairness is a significant and challenging problem in meta-learning. The goal of this problem is to find a set of fairness-aware invariant parameters of classifier that is trained usi
Externí odkaz:
http://arxiv.org/abs/2411.01316
Autor:
Beigi, Mohammad, Wang, Sijia, Shen, Ying, Lin, Zihao, Kulkarni, Adithya, He, Jianfeng, Chen, Feng, Jin, Ming, Cho, Jin-Hee, Zhou, Dawei, Lu, Chang-Tien, Huang, Lifu
In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. As the use of LLMs expands, precisely estimating the uncertainty in their predictions has become crucial. Current metho
Externí odkaz:
http://arxiv.org/abs/2410.20199
Autor:
Zhang, Xueying, Zhang, Bin, Wei, Shihai, Li, Hao, Liao, Jinyu, Zhou, Tao, Deng, Guangwei, Wang, You, Song, Haizhi, You, Lixing, Fan, Boyu, Fan, Yunru, Chen, Feng, Guo, Guangcan, Zhou, Qiang
Light-matter interface is an important building block for long-distance quantum networks. Towards a scalable quantum network with high-rate quantum information processing, it requires to develop integrated light-matter interfaces with broadband and m
Externí odkaz:
http://arxiv.org/abs/2410.18516