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Feb 1, 2024 · Compared to traditional Artificial Neural Network (ANN), Spiking Neural Network (SNN) has garnered widespread academic interest for its intrinsic ability to transmit information in a more energy-efficient manner. However, despite previous efforts to optimize the learning algorithm of SNNs through various methods, SNNs still lag behind ANNs in terms of …
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Feb 20, 2019 · 289-я артиллерийская бригада большой мощности. Military Unit: 02561. Activated 1.12.73 in Luga, Leningrad Oblast. Equipped with 24 203mm guns and 24 240mm mortars. 1980 with 24 203mm 2s7 Pion and 24 240mm 2s4 Tyulpan. From 1989 with 48 2s7 Pion.
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May 25, 2024 · In-context learning (ICL), the remarkable ability to solve a task from only input exemplars, is often assumed to be a unique hallmark of Transformer models. By examining commonly employed synthetic ICL tasks, we demonstrate that multi-layer perceptrons (MLPs) can also learn in-context. Moreover, MLPs, and the closely related MLP-Mixer models, learn in …
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Oct 16, 2024 · Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Classical Analysis and ODEs (math.CA) arXiv:2410.14735 (cross-list from cs.CL) [pdf, html, other] Title: Agent Skill Acquisition for Large Language Models via CycleQD So Kuroki, Taishi ...
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Mar 17, 2024 · Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep learning by emulating the event-driven processing manner of the brain. Incorporating Transformers with SNNs has shown promise for accuracy. However, they struggle to learn high-frequency patterns, such as moving edges and pixel-level brightness changes, …
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