Zobrazeno 1 - 10
of 18 149
pro vyhledávání: '"An Mengyu"'
In this paper, we investigate the convergence rate of the averaging principle for stochastic differential equations (SDEs) with $\beta$-H\"older drift driven by $\alpha$-stable processes. More specifically, we first derive the Schauder estimate for n
Externí odkaz:
http://arxiv.org/abs/2409.12706
This paper introduces a Spiking Diffusion Policy (SDP) learning method for robotic manipulation by integrating Spiking Neurons and Learnable Channel-wise Membrane Thresholds (LCMT) into the diffusion policy model, thereby enhancing computational effi
Externí odkaz:
http://arxiv.org/abs/2409.11195
Autor:
Wang, Mengyu, Ma, Tiejun
It is widely acknowledged that extracting market sentiments from news data benefits market predictions. However, existing methods of using financial sentiments remain simplistic, relying on equal-weight and static aggregation to manage sentiments fro
Externí odkaz:
http://arxiv.org/abs/2409.05698
Circuit knitting emerges as a promising technique to overcome the limitation of the few physical qubits in near-term quantum hardware by cutting large quantum circuits into smaller subcircuits. Recent research in this area has been primarily oriented
Externí odkaz:
http://arxiv.org/abs/2409.03870
Dual function radar and communication (DFRC) is a promising research direction within integrated sensing and communication (ISAC), improving hardware and spectrum efficiency by merging sensing and communication (S&C) functionalities into a shared pla
Externí odkaz:
http://arxiv.org/abs/2408.16455
The loss functions of many learning problems contain multiple additive terms that can disagree and yield conflicting update directions. For Physics-Informed Neural Networks (PINNs), loss terms on initial/boundary conditions and physics equations are
Externí odkaz:
http://arxiv.org/abs/2408.11104
Autor:
Lu, Jiarui, Holleis, Thomas, Zhang, Yizhe, Aumayer, Bernhard, Nan, Feng, Bai, Felix, Ma, Shuang, Ma, Shen, Li, Mengyu, Yin, Guoli, Wang, Zirui, Pang, Ruoming
Recent large language models (LLMs) advancements sparked a growing research interest in tool assisted LLMs solving real-world challenges, which calls for comprehensive evaluation of tool-use capabilities. While previous works focused on either evalua
Externí odkaz:
http://arxiv.org/abs/2408.04682
The many-to-many multilingual neural machine translation can be regarded as the process of integrating semantic features from the source sentences and linguistic features from the target sentences. To enhance zero-shot translation, models need to sha
Externí odkaz:
http://arxiv.org/abs/2408.01394
We first propose a concise singular value decomposition of dual matrices. Then, the randomized version of the decomposition is presented. It can significantly reduce the computational cost while maintaining the similar accuracy. We analyze the theore
Externí odkaz:
http://arxiv.org/abs/2407.16925
Autor:
Liu, Yonghao, Li, Mengyu, Li, Ximing, Huang, Lan, Giunchiglia, Fausto, Liang, Yanchun, Feng, Xiaoyue, Guan, Renchu
Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies have attempted to combine meta-learning with graph neural networks to sol
Externí odkaz:
http://arxiv.org/abs/2407.14732