Zobrazeno 1 - 10
of 61 125
pro vyhledávání: '"li, Xiang"'
The theory of swarm control shows promise for controlling multiple objects, however, scalability is hindered by cost constraints, such as hardware and infrastructure. Virtual Reality (VR) can overcome these limitations, but research on swarm interact
Externí odkaz:
http://arxiv.org/abs/2410.18924
Autor:
Wu, Jiayi, Sun, Hao, Cai, Hengyi, Su, Lixin, Wang, Shuaiqiang, Yin, Dawei, Li, Xiang, Gao, Ming
The number of large language models (LLMs) with varying parameter scales and vocabularies is increasing. While they deliver powerful performance, they also face a set of common optimization needs to meet specific requirements or standards, such as in
Externí odkaz:
http://arxiv.org/abs/2410.17599
Graph Neural Networks (GNNs) have emerged as powerful models for learning from graph-structured data. However, GNNs lack the inherent semantic understanding capability of rich textual nodesattributes, limiting their effectiveness in applications. On
Externí odkaz:
http://arxiv.org/abs/2410.16822
The discrepancies between observations and theoretical predictions of cataclysmic variables (CVs) suggest that there exists unknown angular momentum loss mechanism(s) besides magnetic braking and gravitational radiation. Mass loss due to nova eruptio
Externí odkaz:
http://arxiv.org/abs/2410.15254
Autor:
Chen, Hao, Waheed, Abdul, Li, Xiang, Wang, Yidong, Wang, Jindong, Raj, Bhiksha, Abdin, Marah I.
The rise of Large Language Models (LLMs) has accentuated the need for diverse, high-quality pre-training data. Synthetic data emerges as a viable solution to the challenges of data scarcity and inaccessibility. While previous literature has focused p
Externí odkaz:
http://arxiv.org/abs/2410.15226
In this paper, we introduce DCDepth, a novel framework for the long-standing monocular depth estimation task. Moving beyond conventional pixel-wise depth estimation in the spatial domain, our approach estimates the frequency coefficients of depth pat
Externí odkaz:
http://arxiv.org/abs/2410.14980
Autor:
Xu, Shaoming, Renganathan, Arvind, Khandelwal, Ankush, Ghosh, Rahul, Li, Xiang, Liu, Licheng, Tayal, Kshitij, Harrington, Peter, Jia, Xiaowei, Jin, Zhenong, Nieber, Jonh, Kumar, Vipin
Streamflow, vital for water resource management, is governed by complex hydrological systems involving intermediate processes driven by meteorological forces. While deep learning models have achieved state-of-the-art results of streamflow prediction,
Externí odkaz:
http://arxiv.org/abs/2410.14137
We propose a novel framework, Stable Diffusion-based Momentum Integrated Adversarial Examples (SD-MIAE), for generating adversarial examples that can effectively mislead neural network classifiers while maintaining visual imperceptibility and preserv
Externí odkaz:
http://arxiv.org/abs/2410.13122
Transferable targeted adversarial attacks (TTAs) against deep neural networks have been proven significantly more challenging than untargeted ones, yet they remain relatively underexplored. This paper sheds new light on performing highly efficient ye
Externí odkaz:
http://arxiv.org/abs/2410.13891
Autor:
Jin, Pengfei, Shu, Peng, Kim, Sekeun, Xiao, Qing, Song, Sifan, Chen, Cheng, Liu, Tianming, Li, Xiang, Li, Quanzheng
Foundation models have become a cornerstone in deep learning, with techniques like Low-Rank Adaptation (LoRA) offering efficient fine-tuning of large models. Similarly, methods such as Retrieval-Augmented Generation (RAG), which leverage vectorized d
Externí odkaz:
http://arxiv.org/abs/2410.09908