Zobrazeno 1 - 10
of 442
pro vyhledávání: '"Chen Yuxiao"'
Publikováno v:
Frontiers in Pharmacology, Vol 15 (2024)
BackgroundArginine deprivation therapy (ADT) hinders glioma cells’ access to nutrients by reducing peripheral blood arginine, showing great efficacy in various studies, which suggests it as a potentially promising treatment for glioma. The aim of t
Externí odkaz:
https://doaj.org/article/aa96d2e804644b3c881409c8029b75f8
Federated Learning (FL) is designed to prevent data leakage through collaborative model training without centralized data storage. However, it remains vulnerable to gradient reconstruction attacks that recover original training data from shared gradi
Externí odkaz:
http://arxiv.org/abs/2411.03746
Autor:
Huang, Zhiyu, Weng, Xinshuo, Igl, Maximilian, Chen, Yuxiao, Cao, Yulong, Ivanovic, Boris, Pavone, Marco, Lv, Chen
Autonomous driving necessitates the ability to reason about future interactions between traffic agents and to make informed evaluations for planning. This paper introduces the \textit{Gen-Drive} framework, which shifts from the traditional prediction
Externí odkaz:
http://arxiv.org/abs/2410.05582
Advancements in deep multi-agent reinforcement learning (MARL) have positioned it as a promising approach for decision-making in cooperative games. However, it still remains challenging for MARL agents to learn cooperative strategies for some game en
Externí odkaz:
http://arxiv.org/abs/2410.03997
Autor:
Chen, Yuxiao, Li, Kai, Bao, Wentao, Patel, Deep, Kong, Yu, Min, Martin Renqiang, Metaxas, Dimitris N.
Learning to localize temporal boundaries of procedure steps in instructional videos is challenging due to the limited availability of annotated large-scale training videos. Recent works focus on learning the cross-modal alignment between video segmen
Externí odkaz:
http://arxiv.org/abs/2409.16145
Autor:
Tan, Shuhan, Ivanovic, Boris, Chen, Yuxiao, Li, Boyi, Weng, Xinshuo, Cao, Yulong, Krähenbühl, Philipp, Pavone, Marco
Simulation stands as a cornerstone for safe and efficient autonomous driving development. At its core a simulation system ought to produce realistic, reactive, and controllable traffic patterns. In this paper, we propose ProSim, a multimodal promptab
Externí odkaz:
http://arxiv.org/abs/2409.05863
The quadratic traveling salesperson problem (QTSP) is a generalization of the traveling salesperson problem, in which all triples of consecutive customers in a tour determine the travel cost. We propose compact optimization models for QTSP in mixed-i
Externí odkaz:
http://arxiv.org/abs/2408.16680
Autor:
Li, Boyi, Zhu, Ligeng, Tian, Ran, Tan, Shuhan, Chen, Yuxiao, Lu, Yao, Cui, Yin, Veer, Sushant, Ehrlich, Max, Philion, Jonah, Weng, Xinshuo, Xue, Fuzhao, Tao, Andrew, Liu, Ming-Yu, Fidler, Sanja, Ivanovic, Boris, Darrell, Trevor, Malik, Jitendra, Han, Song, Pavone, Marco
We propose Wolf, a WOrLd summarization Framework for accurate video captioning. Wolf is an automated captioning framework that adopts a mixture-of-experts approach, leveraging complementary strengths of Vision Language Models (VLMs). By utilizing bot
Externí odkaz:
http://arxiv.org/abs/2407.18908
Autor:
Tian, Ran, Li, Boyi, Weng, Xinshuo, Chen, Yuxiao, Schmerling, Edward, Wang, Yue, Ivanovic, Boris, Pavone, Marco
The autonomous driving industry is increasingly adopting end-to-end learning from sensory inputs to minimize human biases in system design. Traditional end-to-end driving models, however, suffer from long-tail events due to rare or unseen inputs with
Externí odkaz:
http://arxiv.org/abs/2407.00959
Generating realistic and controllable agent behaviors in traffic simulation is crucial for the development of autonomous vehicles. This problem is often formulated as imitation learning (IL) from real-world driving data by either directly predicting
Externí odkaz:
http://arxiv.org/abs/2404.02524