Zobrazeno 1 - 10
of 60 161
pro vyhledávání: '"An, Yulong"'
Relaxation to equilibrium in Bose-Hubbard rings is numerically investigated in the time domain. We show that for small mean site populations in the Mott insulator regime, characterized by strong onsite interaction as compared to hopping, equipartitio
Externí odkaz:
http://arxiv.org/abs/2410.06039
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
Autor:
Huang, Yulong, Liu, Zunchang, Feng, Changchun, Lin, Xiaopeng, Ren, Hongwei, Fu, Haotian, Zhou, Yue, Xing, Hong, Cheng, Bojun
Recently, there is growing demand for effective and efficient long sequence modeling, with State Space Models (SSMs) proving to be effective for long sequence tasks. To further reduce energy consumption, SSMs can be adapted to Spiking Neural Networks
Externí odkaz:
http://arxiv.org/abs/2410.03530
We consider a family of variable time-stepping Dahlquist-Liniger-Nevanlinna (DLN) schemes, which is unconditional non-linear stable and second order accurate, for the Allen-Cahn equation. The finite element methods are used for the spatial discretiza
Externí odkaz:
http://arxiv.org/abs/2409.19481
Autor:
Wang, Xinlong, Zhang, Xiaosong, Luo, Zhengxiong, Sun, Quan, Cui, Yufeng, Wang, Jinsheng, Zhang, Fan, Wang, Yueze, Li, Zhen, Yu, Qiying, Zhao, Yingli, Ao, Yulong, Min, Xuebin, Li, Tao, Wu, Boya, Zhao, Bo, Zhang, Bowen, Wang, Liangdong, Liu, Guang, He, Zheqi, Yang, Xi, Liu, Jingjing, Lin, Yonghua, Huang, Tiejun, Wang, Zhongyuan
While next-token prediction is considered a promising path towards artificial general intelligence, it has struggled to excel in multimodal tasks, which are still dominated by diffusion models (e.g., Stable Diffusion) and compositional approaches (e.
Externí odkaz:
http://arxiv.org/abs/2409.18869
Semantic communication technology emerges as a pivotal bridge connecting AI with classical communication. The current semantic communication systems are generally modeled as an Auto-Encoder (AE). AE lacks a deep integration of AI principles with comm
Externí odkaz:
http://arxiv.org/abs/2410.08222
Foundation models for natural language processing, powered by the transformer architecture, exhibit remarkable in-context learning (ICL) capabilities, allowing pre-trained models to adapt to downstream tasks using few-shot prompts without updating th
Externí odkaz:
http://arxiv.org/abs/2409.12293
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
Multi-robot swarms utilize swarm intelligence to collaborate on tasks and play an increasingly significant role in a variety of practical scenarios. However, due to the complex design, multi-robot swarm systems often have vulnerabilities caused by lo
Externí odkaz:
http://arxiv.org/abs/2409.04736
Autor:
Sasaki, Yuya, Wang, Yulong
We introduce a novel method for estimating and conducting inference about extreme quantile treatment effects (QTEs) in the presence of endogeneity. Our approach is applicable to a broad range of empirical research designs, including instrumental vari
Externí odkaz:
http://arxiv.org/abs/2409.03979