atlas

Research map / Optimization / Optimizers

AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients

NeurIPS 2020 · Optimization › Optimizers · approach: Adam convergence and adaptive learning rates · 121 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
AdaBelief instead of AdamResNet18 · ImageNet valtop-1 accuracy: 63.79 -> 70.08Helped
AdaBelief instead of SGDResNet18 · ImageNet valtop-1 accuracy: 70.23 -> 70.08No clear effect
AdaBelief instead of AdamSN-GAN · CIFAR-10 SN-GANFID: 13.05 -> 12.52Helped
AdaBelief instead of AdamWGAN-GP · CIFAR-10 WGAN-GPFID: 73.5 -> 61.8Helped
reduce Padam exponent p (less adaptivity)Padam · CIFAR-10 WGANFIDHurt
AdaBelief instead of AdamTransformer · IWSLT14BLEU: 35.60 -> 35.85Helped
AdaBelief instead of AdaHessianTransformer · IWSLT14BLEU: 35.90 -> 35.85No clear effect
AdaBelief instead of SGDFaster-RCNN · PASCAL VOCmAP: 79.50 -> 81.02Helped
AdaBelief instead of other optimizers2/3-layer LSTM · Penn TreebankperplexityHelped
AdaBelief instead of other optimizers1-layer LSTM · Penn TreebankperplexityNo 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