Memory Efficient Neural Processes via Constant Memory Attention Block

22 Sept 2023 (modified: 11 Feb 2024)Submitted to ICLR 2024EveryoneRevisionsBibTeX
Primary Area: general machine learning (i.e., none of the above)
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Keywords: Attention, Neural Processes, Meta-learning, Efficiency
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TL;DR: We propose (1) the Constant Memory Block, a novel attention block that only requires constant memory, and (2) Constant Memory Attentive Neural Processes, a Neural Processes variant that achieves state-of-the-art results while being more efficient.
Abstract: Neural Processes (NPs) are popular meta-learning methods for efficiently modelling predictive uncertainty. Recent state-of-the-art methods, however, leverage expensive attention mechanisms, limiting their applications, particularly in low-resource settings. In this work, we propose Constant Memory Attention Block (CMAB), a novel general-purpose attention block that (1) is permutation invariant, (2) computes its output in constant memory, and (3) performs updates in constant computation. Building on CMAB, we propose Constant Memory Attentive Neural Processes (CMANPs), an NP variant which only requires **constant** memory. Empirically, we show CMANPs achieve state-of-the-art results on popular NP benchmarks (meta-regression and image completion) while being significantly more memory efficient than prior methods.
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Submission Number: 6544