mlsanityData to 14 Oct 2026

Fast Optimizer Benchmark

arXiv 2406.18701 ↗First posted 26 Jun 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

In this paper, we present the Fast Optimizer Benchmark (FOB), a tool designed for evaluating deep learning optimizers during their development. The benchmark supports tasks from multiple domains such as computer vision, natural language processing, and graph learning. The focus is on convenient usage, featuring human-readable YAML configurations, SLURM integration, and plotting utilities. FOB can be used together with existing hyperparameter optimization (HPO) tools as it handles training and resuming of runs. The modular design enables integration into custom pipelines, using it simply as a collection of tasks. We showcase an optimizer comparison as a usage example of our tool. FOB can be found on GitHub: https://github.com/automl/FOB.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 41 references that are in the map. First posted on arXiv 26 Jun 2024.

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References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  4. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  5. 2018How Powerful are Graph Neural Networks?cited by 4,393
  6. 2016Wide Residual Networkscited by 3,070
  7. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
  8. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  9. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  10. 2018Adafactor: Adaptive Learning Rates with Sublinear Memory Costcited by 905
  11. 2021Revisiting Deep Learning Models for Tabular Datacited by 740
  12. 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 699
6 more
  1. 2017A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasetscited by 377
  2. 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimizationcited by 230
  3. 2019MLPerf Training Benchmarkcited by 115
  4. 2023Benchmarking Neural Network Training Algorithmscited by 52
  5. 2019DeepOBS: A Deep Learning Optimizer Benchmark Suitecited by 37
  6. 2022Benchopt: Reproducible, efficient and collaborative optimization benchmarkscited by 27

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