mlsanityData to 7 Oct 2026

Scalable Batch Acquisition for Deep Bayesian Active Learning

arXiv 2301.05490 ↗First posted 13 Jan 2023cs.LG stat.MLcited by 2

Not on the map. Why?

Abstract (arXiv)

In deep active learning, it is especially important to choose multiple examples to markup at each step to work efficiently, especially on large datasets. At the same time, existing solutions to this problem in the Bayesian setup, such as BatchBALD, have significant limitations in selecting a large number of examples, associated with the exponential complexity of computing mutual information for joint random variables. We, therefore, present the Large BatchBALD algorithm, which gives a well-grounded approximation to the BatchBALD method that aims to achieve comparable quality while being more computationally efficient. We provide a complexity analysis of the algorithm, showing a reduction in computation time, especially for large batches. Furthermore, we present an extensive set of experimental results on image and text data, both on toy datasets and larger ones such as CIFAR-100.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 38 references that are in the map. First posted on arXiv 13 Jan 2023.

Builds on

14 of 38 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  4. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  5. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  6. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  7. 2017Deep Bayesian Active Learning with Image Datacited by 812
  8. 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 602
  9. 2020A Survey of Deep Active Learningcited by 547
  10. 2019A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysiscited by 150
  11. 2021Batch Active Learning at Scalecited by 127
  12. 2018Dropout-based Active Learning for Regressioncited by 24
2 more
  1. 2019'Zhores' -- Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technologycited by 23
  2. 2019Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learningcited by 4

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2024Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraphcited by 105
  2. 2023Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learningcited by 2