Zobrazeno 1 - 10
of 49 454
pro vyhledávání: '"Chen,Hong"'
We consider mean-field vector spin glasses with possibly non-convex interactions. Up to a small perturbation of the parameters defining the model, the asymptotic behavior of the Gibbs measure is described in terms of a critical point of an explicit f
Externí odkaz:
http://arxiv.org/abs/2411.14105
Autor:
Chen, Hong-Bin
In [arXiv:2311.08980], it was shown that if the limit of the free energy in a non-convex vector spin glass model exists, it must be a critical value of a certain functional. In this work, we extend this result to multi-species spin glass models with
Externí odkaz:
http://arxiv.org/abs/2411.13342
Autor:
Guo, Xiang-Fei, Liu, Jian-Wei, Chen, Hong-Xiang, Shi, Fu-Long, Chen, Xiao-Dong, Dong, Jian-Wen
Research on two-dimensional van der Waals materials has demonstrated that the layer degree of freedom can significantly alter the physical properties of materials due to the substantial modification of bulk bands. Inspired by this concept, layered ph
Externí odkaz:
http://arxiv.org/abs/2411.11381
Large Language Models (LLMs) have revolutionized natural language processing by unifying tasks into text generation, yet their large parameter sizes and autoregressive nature limit inference speed. SAM-Decoding addresses this by introducing a novel r
Externí odkaz:
http://arxiv.org/abs/2411.10666
Large language models (LLMs) based on the Transformer architecture are widely employed across various domains and tasks. However, their increasing size imposes significant hardware demands, limiting practical deployment. To mitigate this, model pruni
Externí odkaz:
http://arxiv.org/abs/2411.10272
Developing asynchronous neuromorphic hardware to meet the demands of diverse real-life edge scenarios remains significant challenges. These challenges include constraints on hardware resources and power budgets while satisfying the requirements for r
Externí odkaz:
http://arxiv.org/abs/2411.06059
We investigate the construction of gradient-guided conditional diffusion models for reconstructing private images, focusing on the adversarial interplay between differential privacy noise and the denoising capabilities of diffusion models. While curr
Externí odkaz:
http://arxiv.org/abs/2411.03053
Massive black hole binaries are one of the important sources for the TianQin project. Our research has revealed that, for TianQin, the signal-to-noise ratio squared during the inspiral phase of massive black hole binaries exhibits a direct proportion
Externí odkaz:
http://arxiv.org/abs/2410.19401
Autor:
Chen, Houlun, Wang, Xin, Chen, Hong, Zhang, Zeyang, Feng, Wei, Huang, Bin, Jia, Jia, Zhu, Wenwu
Existing Video Corpus Moment Retrieval (VCMR) is limited to coarse-grained understanding, which hinders precise video moment localization when given fine-grained queries. In this paper, we propose a more challenging fine-grained VCMR benchmark requir
Externí odkaz:
http://arxiv.org/abs/2410.08593
Generalized additive models (GAM) have been successfully applied to high dimensional data analysis. However, most existing methods cannot simultaneously estimate the link function, the component functions and the variable interaction. To alleviate th
Externí odkaz:
http://arxiv.org/abs/2410.06012