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pro vyhledávání: '"Niu, Yue"'
Large Language Models (LLMs) are currently pre-trained and fine-tuned on large cloud servers. The next frontier is LLM personalization, where a foundation model can be fine-tuned with user/task-specific data. Given the sensitive nature of such privat
Externí odkaz:
http://arxiv.org/abs/2409.15520
Autor:
Mehradfar, Asal, Zhao, Xuzhe, Niu, Yue, Babakniya, Sara, Alesheikh, Mahdi, Aghasi, Hamidreza, Avestimehr, Salman
Analog and radio-frequency circuit design requires extensive exploration of both circuit topology and parameters to meet specific design criteria like power consumption and bandwidth. Designers must review state-of-the-art topology configurations in
Externí odkaz:
http://arxiv.org/abs/2407.18272
Publikováno v:
IEEE Transactions on Mobile Computing, Early Access, (2024)
In Federated Learning (FL), clients may have weak devices that cannot train the full model or even hold it in their memory space. To implement large-scale FL applications, thus, it is crucial to develop a distributed learning method that enables the
Externí odkaz:
http://arxiv.org/abs/2406.15125
We study a cost-aware programming language for higher-order recursion dubbed $\textbf{PCF}_\mathsf{cost}$ in the setting of synthetic domain theory (SDT). Our main contribution relates the denotational cost semantics of $\textbf{PCF}_\mathsf{cost}$ t
Externí odkaz:
http://arxiv.org/abs/2404.00212
Graph neural networks (GNNs) play a key role in learning representations from graph-structured data and are demonstrated to be useful in many applications. However, the GNN training pipeline has been shown to be vulnerable to node feature leakage and
Externí odkaz:
http://arxiv.org/abs/2403.10995
Language models (LMs) have greatly propelled the research on natural language processing. However, LMs also raise concerns regarding the generation of biased or toxic content and the potential disclosure of private information from the training datas
Externí odkaz:
http://arxiv.org/abs/2403.08994
We propose a new attention mechanism with linear complexity, ATP, that fixates \textbf{A}ttention on \textbf{T}op \textbf{P}rincipal keys, rather than on each individual token. Particularly, ATP is driven by an important observation that input sequen
Externí odkaz:
http://arxiv.org/abs/2403.02352
Data privacy is of great concern in cloud machine-learning service platforms, when sensitive data are exposed to service providers. While private computing environments (e.g., secure enclaves), and cryptographic approaches (e.g., homomorphic encrypti
Externí odkaz:
http://arxiv.org/abs/2312.05264
Spectral-domain CNNs have been shown to be more efficient than traditional spatial CNNs in terms of reducing computation complexity. However they come with a `kernel explosion' problem that, even after compression (pruning), imposes a high memory bur
Externí odkaz:
http://arxiv.org/abs/2310.10902
Quasi-Newton methods still face significant challenges in training large-scale neural networks due to additional compute costs in the Hessian related computations and instability issues in stochastic training. A well-known method, L-BFGS that efficie
Externí odkaz:
http://arxiv.org/abs/2307.13744