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Akademický článek
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We investigate stochastic utility maximization games under relative performance concerns in both finite-agent and infinite-agent (graphon) settings. An incomplete market model is considered where agents with power (CRRA) utility functions trade in a
Externí odkaz:
http://arxiv.org/abs/2412.03070
Prediction-based (PB) inference is increasingly used in applications where the outcome of interest is difficult to obtain, but its predictors are readily available. Unlike traditional inference, PB inference performs statistical inference using a par
Externí odkaz:
http://arxiv.org/abs/2411.19908
In medical image analysis, achieving fast, efficient, and accurate segmentation is essential for automated diagnosis and treatment. Although recent advancements in deep learning have significantly improved segmentation accuracy, current models often
Externí odkaz:
http://arxiv.org/abs/2411.19447
Autor:
Zhao, Yaqi, Yin, Yuanyang, Li, Lin, Lin, Mingan, Huang, Victor Shea-Jay, Chen, Siwei, Chen, Weipeng, Yin, Baoqun, Zhou, Zenan, Zhang, Wentao
Does seeing always mean knowing? Large Vision-Language Models (LVLMs) integrate separately pre-trained vision and language components, often using CLIP-ViT as vision backbone. However, these models frequently encounter a core issue of "cognitive misa
Externí odkaz:
http://arxiv.org/abs/2411.16824
Autor:
Yin, Yuanyang, Zhao, Yaqi, Zheng, Mingwu, Lin, Ke, Ou, Jiarong, Chen, Rui, Huang, Victor Shea-Jay, Wang, Jiahao, Tao, Xin, Wan, Pengfei, Zhang, Di, Yin, Baoqun, Zhang, Wentao, Gai, Kun
Achieving optimal performance of video diffusion transformers within given data and compute budget is crucial due to their high training costs. This necessitates precisely determining the optimal model size and training hyperparameters before large-s
Externí odkaz:
http://arxiv.org/abs/2411.17470
Autor:
Wang, Yaqi, Xu, Haipei
Recently, as Large Language Models (LLMs) have shown impressive emerging capabilities and gained widespread popularity, research on LLM-based search agents has proliferated. In real-world situations, users often input contextual and highly personaliz
Externí odkaz:
http://arxiv.org/abs/2411.14574
We designed a Retrieval-Augmented Generation (RAG) system to provide large language models with relevant documents for answering domain-specific questions about Pittsburgh and Carnegie Mellon University (CMU). We extracted over 1,800 subpages using a
Externí odkaz:
http://arxiv.org/abs/2411.13691
Autor:
Wang, Zirui, Zhao, Xinran, Stepputtis, Simon, Kim, Woojun, Wu, Tongshuang, Sycara, Katia, Xie, Yaqi
Understanding and predicting human actions has been a long-standing challenge and is a crucial measure of perception in robotics AI. While significant progress has been made in anticipating the future actions of individual agents, prior work has larg
Externí odkaz:
http://arxiv.org/abs/2411.01455
Autor:
Li, Bowen, Li, Zhaoyu, Du, Qiwei, Luo, Jinqi, Wang, Wenshan, Xie, Yaqi, Stepputtis, Simon, Wang, Chen, Sycara, Katia P., Ravikumar, Pradeep Kumar, Gray, Alexander G., Si, Xujie, Scherer, Sebastian
Recent years have witnessed the rapid development of Neuro-Symbolic (NeSy) AI systems, which integrate symbolic reasoning into deep neural networks. However, most of the existing benchmarks for NeSy AI fail to provide long-horizon reasoning tasks wit
Externí odkaz:
http://arxiv.org/abs/2411.00773