Zobrazeno 1 - 10
of 4 641
pro vyhledávání: '"WU, You"'
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
We search for a stochastic gravitational-wave background (SGWB) originating from scalar-induced gravitational waves (SIGWs) with the sound speed resonance (SSR) effect using data from Advanced LIGO and Advanced Virgo's first three observing runs. The
Externí odkaz:
http://arxiv.org/abs/2409.14929
The imbalance of exploration and exploitation has long been a significant challenge in reinforcement learning. In policy optimization, excessive reliance on exploration reduces learning efficiency, while over-dependence on exploitation might trap age
Externí odkaz:
http://arxiv.org/abs/2408.09974
The innovative GNN-CL model proposed in this paper marks a breakthrough in the field of financial fraud detection by synergistically combining the advantages of graph neural networks (gnn), convolutional neural networks (cnn) and long short-term memo
Externí odkaz:
http://arxiv.org/abs/2407.06529
Despite the wealth of single-cell multi-omics data, it remains challenging to predict the consequences of novel genetic and chemical perturbations in the human body. It requires knowledge of molecular interactions at all biological levels, encompassi
Externí odkaz:
http://arxiv.org/abs/2407.06405
Object detection has greatly improved over the past decade thanks to advances in deep learning and large-scale datasets. However, detecting objects reflected in surfaces remains an underexplored area. Reflective surfaces are ubiquitous in daily life,
Externí odkaz:
http://arxiv.org/abs/2407.05575
Recently, the surge in the adoption of single-stream architectures utilizing pre-trained ViT backbones represents a promising advancement in the field of generic visual tracking. By integrating feature extraction and fusion into a cohesive framework,
Externí odkaz:
http://arxiv.org/abs/2407.05383