Zobrazeno 1 - 10
of 368 884
pro vyhledávání: '"theoretical perspective"'
In this article, from the viewpoint of control theory, we discuss the relationships among the commonly used monotonicity conditions that ensure the well-posedness of the solutions arising from problems of mean field games (MFGs) and mean field type c
Externí odkaz:
http://arxiv.org/abs/2412.05189
Transformer-based autoregressive sampling has been the major bottleneck for slowing down large language model inferences. One effective way to accelerate inference is \emph{Speculative Decoding}, which employs a small model to sample a sequence of dr
Externí odkaz:
http://arxiv.org/abs/2411.00841
In this paper, we conduct a theoretical analysis of how to integrate reconfigurable intelligent surfaces (RIS) with cooperative non-orthogonal multiple access (NOMA), considering URLLC. We consider a downlink two-user cooperative NOMA system employin
Externí odkaz:
http://arxiv.org/abs/2410.17609
While deep learning has expanded the possibilities for highly expressive variational families, the practical benefits of these tools for variational inference (VI) are often limited by the minimization of the traditional Kullback-Leibler objective, w
Externí odkaz:
http://arxiv.org/abs/2410.13300
Autor:
Sampedro, Juan Carlos
In this paper we use abstract bifurcation theory for Fredholm operators of index zero to deal with periodic even solutions of the one-dimensional equation $\mathcal{L}u=\lambda u+|u|^{p}$, where $\mathcal{L}$ is a nonlocal pseudodifferential operator
Externí odkaz:
http://arxiv.org/abs/2409.04253
Autor:
Wangni, Jianqiao
Comparing to deep neural networks trained for specific tasks, those foundational deep networks trained on generic datasets such as ImageNet classification, benefits from larger-scale datasets, simpler network structure and easier training techniques.
Externí odkaz:
http://arxiv.org/abs/2409.10555
Federated learning (FL) is an emerging collaborative learning paradigm that aims to protect data privacy. Unfortunately, recent works show FL algorithms are vulnerable to the serious data reconstruction attacks. However, existing works lack a theoret
Externí odkaz:
http://arxiv.org/abs/2408.12119
The versatility of self-attention mechanism earned transformers great success in almost all data modalities, with limitations on the quadratic complexity and difficulty of training. To apply transformers across different data modalities, practitioner
Externí odkaz:
http://arxiv.org/abs/2408.05822