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pro vyhledávání: '"Adaptive Computation"'
Autor:
Alizadeh, Keivan, Mirzadeh, Iman, Shahrokhi, Hooman, Belenko, Dmitry, Sun, Frank, Cho, Minsik, Sekhavat, Mohammad Hossein, Nabi, Moin, Farajtabar, Mehrdad
Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget, leading to inefficient resource utilization. To address this shortcoming, recent advancements in mixture of expert (MoE) models, speculative decoding
Externí odkaz:
http://arxiv.org/abs/2410.10846
Motivated by the drawbacks of cloud-based federated learning (FL), cooperative federated edge learning (CFEL) has been proposed to improve efficiency for FL over mobile edge networks, where multiple edge servers collaboratively coordinate the distrib
Externí odkaz:
http://arxiv.org/abs/2409.04022
While transformer models have been highly successful, they are computationally inefficient. We observe that for each layer, the full width of the layer may be needed only for a small subset of tokens inside a batch and that the "effective" width need
Externí odkaz:
http://arxiv.org/abs/2312.10193
CoTFormer: A Chain-of-Thought Driven Architecture with Budget-Adaptive Computation Cost at Inference
Scaling language models to larger and deeper sizes has led to significant boosts in performance. Even though the size of these models limits their application in compute-constrained environments, the race to continually develop ever larger and deeper
Externí odkaz:
http://arxiv.org/abs/2310.10845
In this paper, we discuss adaptive approximations of an elliptic eigenvalue optimization problem in a phase-field setting by a conforming finite element method. An adaptive algorithm is proposed and implemented in several two dimensional numerical ex
Externí odkaz:
http://arxiv.org/abs/2310.03970
Akademický článek
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Diffusion models achieve great success in generating diverse and high-fidelity images, yet their widespread application, especially in real-time scenarios, is hampered by their inherently slow generation speed. The slow generation stems from the nece
Externí odkaz:
http://arxiv.org/abs/2309.17074
Akademický článek
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Autor:
Xue, Fuzhao, Likhosherstov, Valerii, Arnab, Anurag, Houlsby, Neil, Dehghani, Mostafa, You, Yang
Humans have the ability to adapt the type of information they use, the procedure they employ, and the amount of time they spend when solving problems. However, most standard neural networks have a fixed function type and computation budget regardless
Externí odkaz:
http://arxiv.org/abs/2301.13195