atlas

Research map / Architectures and efficiency / RNNs and normalization

Memorization Capacity of Neural Networks with Conditional Computation

ICLR 2023 · Architectures and efficiency › RNNs and normalization · approach: Theory of depth and width · 0 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use conditional computation (synthesized conditional network) instead of unconditional network for memorizationReLU memorization network · Memorization of n patternsoperations per input: O(sqrt(n)) to O(log n)Helped
Theoretical lower bound: any conditional network needs Omega(log n) operations per input under rank conditionConditional ReLU networks · Memorization of n patternsoperations per inputNo clear effect

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy