mlsanityData to 14 Oct 2026

Accelerating Convergence in Bayesian Few-Shot Classification

arXiv 2405.01507 ↗First posted 2 May 2024cs.LG stat.MLcited by 1

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Abstract (arXiv)

Bayesian few-shot classification has been a focal point in the field of few-shot learning. This paper seamlessly integrates mirror descent-based variational inference into Gaussian process-based few-shot classification, addressing the challenge of non-conjugate inference. By leveraging non-Euclidean geometry, mirror descent achieves accelerated convergence by providing the steepest descent direction along the corresponding manifold. It also exhibits the parameterization invariance property concerning the variational distribution. Experimental results demonstrate competitive classification accuracy, improved uncertainty quantification, and faster convergence compared to baseline models. Additionally, we investigate the impact of hyperparameters and components. Code is publicly available at https://github.com/keanson/MD-BSFC.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 48 references that are in the map. First posted on arXiv 2 May 2024.

Builds on

23 of 48 references.

References · 23

  1. 2017On Calibration of Modern Neural Networkscited by 4,960
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  4. 2016Matching Networks for One Shot Learningcited by 2,255
  5. 2016Variational Inference: A Review for Statisticianscited by 1,472
  6. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,287
  7. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
  8. 2016End to End Learning for Self-Driving Carscited by 1,009
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11 more
  1. 2018Probabilistic Model-Agnostic Meta-Learningcited by 256
  2. 2018Bayesian Model-Agnostic Meta-Learningcited by 194
  3. 2018Recasting Gradient-Based Meta-Learning as Hierarchical Bayescited by 180
  4. 2017Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Modelscited by 65
  5. 2013The Information Geometry of Mirror Descentcited by 54
  6. 2018Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Modelscited by 38
  7. 2020Bayesian Few-Shot Classification with One-vs-Each P'olya-Gamma Augmented Gaussian Processescited by 32
  8. 2019Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentationcited by 16
  9. 2016One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilitiescited by 14
  10. 2020Information Theoretic Meta Learning with Gaussian Processescited by 11
  11. 2023Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classificationcited by 6

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Citing papers per year: 2025: 1.

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  1. 2025Color as the Impetus: Transforming Few-Shot Learnercited by 0