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pro vyhledávání: '"WU, You"'
This document presents an in-depth examination of stock market sentiment through the integration of Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU), enabling precise risk alerts. The robust feature extraction capability of CNN is
Externí odkaz:
http://arxiv.org/abs/2412.10199
We search for the stochastic gravitational-wave background (SGWB) predicted by pre-big-bang (PBB) cosmology using data from the first three observing runs of Advanced LIGO and Advanced Virgo. PBB cosmology proposes an alternative to cosmic inflation
Externí odkaz:
http://arxiv.org/abs/2412.09461
This paper aims to study the prediction of the bank stability index based on the Time Series Transformer model. The bank stability index is an important indicator to measure the health status and risk resistance of financial institutions. Traditional
Externí odkaz:
http://arxiv.org/abs/2412.03606
Harnessing low-light enhancement and domain adaptation, nighttime UAV tracking has made substantial strides. However, over-reliance on image enhancement, scarcity of high-quality nighttime data, and neglecting the relationship between daytime and nig
Externí odkaz:
http://arxiv.org/abs/2412.00626
We investigate whether the Pre-Big Bang (PBB) scenario from string cosmology can explain the stochastic gravitational wave background signal reported in the NANOGrav 15-year dataset. Using Bayesian analysis techniques, we constrain the key parameters
Externí odkaz:
http://arxiv.org/abs/2411.16505
Recently, sharing key-value (KV) cache across layers has been found effective in efficient inference of large language models (LLMs). To systematically investigate different techniques of cross-layer KV sharing, we propose a unified framework that co
Externí odkaz:
http://arxiv.org/abs/2410.14442
Autor:
Liang, Yi, Wu, You, Zhuang, Honglei, Chen, Li, Shen, Jiaming, Jia, Yiling, Qin, Zhen, Sanghai, Sumit, Wang, Xuanhui, Yang, Carl, Bendersky, Michael
Generating high-quality, in-depth textual documents, such as academic papers, news articles, Wikipedia entries, and books, remains a significant challenge for Large Language Models (LLMs). In this paper, we propose to use planning to generate long fo
Externí odkaz:
http://arxiv.org/abs/2410.06203
In sparse reward scenarios of reinforcement learning (RL), the memory mechanism provides promising shortcuts to policy optimization by reflecting on past experiences like humans. However, current memory-based RL methods simply store and reuse high-va
Externí odkaz:
http://arxiv.org/abs/2410.04498
Multi-label data stream usually contains noisy labels in the real-world applications, namely occuring in both relevant and irrelevant labels. However, existing online multi-label classification methods are mostly limited in terms of label quality and
Externí odkaz:
http://arxiv.org/abs/2410.02394
The North American Nanohertz Observatory for Gravitational Waves (NANOGrav) collaboration has recently reported strong evidence for a signal at nanohertz, potentially the first detection of the stochastic gravitational-wave background (SGWB). We inve
Externí odkaz:
http://arxiv.org/abs/2409.17846