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DISTA: Denoising Spiking Transformer with intrinsic plasticity and spatiotemporal attention
We introduce DISTA, a Denoising Spiking Transformer with Intrinsic Plasticity and SpatioTemporal Attention, designed to maximize the spatiotemporal computational prowess of spiking neurons, particularly for vision applications.
UPAR: A Kantian-Inspired Prompting Framework for Enhancing Large Language Model Capabilities
Large Language Models (LLMs) have demonstrated impressive inferential capabilities, with numerous research endeavors devoted to enhancing this capacity through prompting.
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