Zobrazeno 1 - 10
of 845 381
pro vyhledávání: '"Chi, AS"'
Autor:
Huang, Ning-Chi, Chang, Chi-Chih, Lin, Wei-Cheng, Taka, Endri, Marculescu, Diana, Wu, Kai-Chiang
$N{:}M$ sparsity is an emerging model compression method supported by more and more accelerators to speed up sparse matrix multiplication in deep neural networks. Most existing $N{:}M$ sparsity methods compress neural networks with a uniform setting
Externí odkaz:
http://arxiv.org/abs/2409.09708
Hyperspectral image (HSI) classification involves assigning specific labels to each pixel to identify various land cover categories. Although deep classifiers have shown high predictive accuracy in this field, quantifying their uncertainty remains a
Externí odkaz:
http://arxiv.org/abs/2409.01236
Autor:
Zhang, Yiming, Rando, Javier, Evtimov, Ivan, Chi, Jianfeng, Smith, Eric Michael, Carlini, Nicholas, Tramèr, Florian, Ippolito, Daphne
Large language models are pre-trained on uncurated text datasets consisting of trillions of tokens scraped from the Web. Prior work has shown that: (1) web-scraped pre-training datasets can be practically poisoned by malicious actors; and (2) adversa
Externí odkaz:
http://arxiv.org/abs/2410.13722
Applying the Velocity Gradient Technique in NGC 1333: Comparison with Dust Polarization Observations
Magnetic fields (B-fields) are ubiquitous in the interstellar medium (ISM), and they play an essential role in the formation of molecular clouds and subsequent star formation. However, B-fields in interstellar environments remain challenging to measu
Externí odkaz:
http://arxiv.org/abs/2410.13350
State Space Models (SSMs) have emerged as an appealing alternative to Transformers for large language models, achieving state-of-the-art accuracy with constant memory complexity which allows for holding longer context lengths than attention-based net
Externí odkaz:
http://arxiv.org/abs/2410.13229
In this paper, we study a class of degenerate mean field games (MFGs) with state-distribution dependent and unbounded functional diffusion coefficients. With a probabilistic method, we study the well-posedness of the forward-backward stochastic diffe
Externí odkaz:
http://arxiv.org/abs/2410.12404
Autor:
Lau, Jason, Xiao, Yuanlong, Xie, Yutong, Chi, Yuze, Song, Linghao, Xiang, Shaojie, Lo, Michael, Zhang, Zhiru, Cong, Jason, Guo, Licheng
Publikováno v:
IEEE/ACM International Conference on Computer-Aided Design (2024), October 27-31, New York, NY, USA. ACM, New York, NY, USA, 11 pages
The increasing complexity of large-scale FPGA accelerators poses significant challenges in achieving high performance while maintaining design productivity. High-level synthesis (HLS) has been adopted as a solution, but the mismatch between the high-
Externí odkaz:
http://arxiv.org/abs/2410.13079
Autor:
Tang, Zhenheng, Kang, Xueze, Yin, Yiming, Pan, Xinglin, Wang, Yuxin, He, Xin, Wang, Qiang, Zeng, Rongfei, Zhao, Kaiyong, Shi, Shaohuai, Zhou, Amelie Chi, Li, Bo, He, Bingsheng, Chu, Xiaowen
To alleviate hardware scarcity in training large deep neural networks (DNNs), particularly large language models (LLMs), we present FusionLLM, a decentralized training system designed and implemented for training DNNs using geo-distributed GPUs acros
Externí odkaz:
http://arxiv.org/abs/2410.12707
We present a unified deterministic approach for experimental design problems using the method of interlacing polynomials. Our framework recovers the best-known approximation guarantees for the well-studied D/A/E-design problems with simple analysis.
Externí odkaz:
http://arxiv.org/abs/2410.11390
Autor:
Gao, Shangqian, Lin, Chi-Heng, Hua, Ting, Zheng, Tang, Shen, Yilin, Jin, Hongxia, Hsu, Yen-Chang
Large Language Models (LLMs) have achieved remarkable success in various natural language processing tasks, including language modeling, understanding, and generation. However, the increased memory and computational costs associated with these models
Externí odkaz:
http://arxiv.org/abs/2410.11988