Zobrazeno 1 - 10
of 5 506
pro vyhledávání: '"YIN HANG"'
Artificial intelligence generated content (AIGC) technologies, with a predominance of large language models (LLMs), have demonstrated remarkable performance improvements in various applications, which have attracted great interests from both academia
Externí odkaz:
http://arxiv.org/abs/2411.06193
Kelvin Probe Force Microscopy (KPFM) is widely used to measure the surface potential on samples, from which electrostatic patch force can be calculated. However, since the KPFM measurements represent a weighted average of local potentials on the samp
Externí odkaz:
http://arxiv.org/abs/2411.01733
Spike train classification has recently become an important topic in the machine learning community, where each spike train is a binary event sequence with \emph{temporal-sparsity of signals of interest} and \emph{temporal-noise} properties. A promis
Externí odkaz:
http://arxiv.org/abs/2411.05806
In this paper, we propose a new framework for zero-shot object navigation. Existing zero-shot object navigation methods prompt LLM with the text of spatially closed objects, which lacks enough scene context for in-depth reasoning. To better preserve
Externí odkaz:
http://arxiv.org/abs/2410.08189
Autor:
Reichlin, Alfredo, Tegnér, Gustaf, Vasco, Miguel, Yin, Hang, Björkman, Mårten, Kragic, Danica
Given a finite set of sample points, meta-learning algorithms aim to learn an optimal adaptation strategy for new, unseen tasks. Often, this data can be ambiguous as it might belong to different tasks concurrently. This is particularly the case in me
Externí odkaz:
http://arxiv.org/abs/2410.01476
Autor:
Cai, Xiaofan, Chen, Ruichang, Gao, Xu, Yuan, Meili, Hu, Haixia, Yin, Hang, Qu, Yuanyuan, Tan, Yang, Chen, Feng
Recently, avalanche multiplication has been observed in TMDC-based FETs, enhancing sensor performance with high sensitivity. However, the high voltage required for operation can damage the FETs, making it crucial to reduce the breakdown voltage for e
Externí odkaz:
http://arxiv.org/abs/2409.07677
This report evaluates the performance impact of enabling Trusted Execution Environments (TEE) on NVIDIA Hopper GPUs for large language model (LLM) inference tasks. We benchmark the overhead introduced by TEE mode across various LLMs and token lengths
Externí odkaz:
http://arxiv.org/abs/2409.03992
Autor:
Longhini, Alberta, Wang, Yufei, Garcia-Camacho, Irene, Blanco-Mulero, David, Moletta, Marco, Welle, Michael, Alenyà, Guillem, Yin, Hang, Erickson, Zackory, Held, David, Borràs, Júlia, Kragic, Danica
The realm of textiles spans clothing, households, healthcare, sports, and industrial applications. The deformable nature of these objects poses unique challenges that prior work on rigid objects cannot fully address. The increasing interest within th
Externí odkaz:
http://arxiv.org/abs/2407.01361
Autor:
Nabizadeh, Mohammad Sina, Roy-Chowdhury, Ritoban, Yin, Hang, Ramamoorthi, Ravi, Chern, Albert
We propose Coadjoint Orbit FLIP (CO-FLIP), a high order accurate, structure preserving fluid simulation method in the hybrid Eulerian-Lagrangian framework. We start with a Hamiltonian formulation of the incompressible Euler Equations, and then, using
Externí odkaz:
http://arxiv.org/abs/2406.01936
By concatenating a polar transform with a convolutional transform, polarization-adjusted convolutional (PAC) codes can reach the dispersion approximation bound in certain rate cases. However, the sequential decoding nature of traditional PAC decoding
Externí odkaz:
http://arxiv.org/abs/2405.02590