mlsanityData to 7 Oct 2026

Bayes in the age of intelligent machines

arXiv 2311.10206 ↗First posted 16 Nov 2023cs.LG cs.AIcited by 22

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Abstract (arXiv)

The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference. We argue that this is not the case, and that in fact these systems offer new opportunities for Bayesian modeling. Specifically, we argue that Bayesian models of cognition and artificial neural networks lie at different levels of analysis and are complementary modeling approaches, together offering a way to understand human cognition that spans these levels. We also argue that the same perspective can be applied to intelligent machines, where a Bayesian approach may be uniquely valuable in understanding the behavior of large, opaque artificial neural networks that are trained on proprietary data.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 11 of its 44 references that are in the map. First posted on arXiv 16 Nov 2023.

Builds on

11 of 44 references.

References · 11

  1. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2018Recasting Gradient-Based Meta-Learning as Hierarchical Bayescited by 180
  4. 2023Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solvecited by 175
  5. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 156
  6. 2023Pretraining task diversity and the emergence of non-Bayesian in-context learning for regressioncited by 138
  7. 2023Modeling rapid language learning by distilling Bayesian priors into artificial neural networkscited by 40
  8. 2020Meta-trained agents implement Bayes-optimal agentscited by 37
  9. 2017One Model for the Learning of Languagecited by 21
  10. 2023Gaussian Process Probes (GPP) for Uncertainty-Aware Probingcited by 11
  11. 2022Gaussian Process Surrogate Models for Neural Networkscited by 5

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Citing papers per year: 2024: 10; 2025: 8; 2026: 4.

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  4. 2024Incoherent Probability Judgments in Large Language Modelscited by 29
  5. 2024Bias Amplification in Language Model Evolution: An Iterated Learning Perspectivecited by 17
  6. 2025Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systemscited by 13
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  8. 2025Shaping Shared Languages: Human and Large Language Models' Inductive Biases in Emergent Communicationcited by 9
  9. 2025Levels of Analysis for Large Language Modelscited by 9
  10. 2025Using Reinforcement Learning to Train Large Language Models to Explain Human Decisionscited by 8
  11. 2024On Inductive Biases That Enable Generalization of Diffusion Transformerscited by 7
  12. 2025Identifying and Mitigating the Influence of the Prior Distribution in Large Language Modelscited by 4
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  4. 2025Prompting as Scientific Inquirycited by 2
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  6. 2026Datasets for Verb Alternations across Languages: BLM Templates and Data Augmentation Strategiescited by 1
  7. 2024Feature Fusion for Improved Classification: Combining Dempster-Shafer Theory and Multiple CNN Architecturescited by 0
  8. 2024Bi-ICE: An Inner Interpretable Framework for Image Classification via Bi-directional Interactions between Concept and Input Embeddingscited by 0
  9. 2026Comparing Natural and Synthetic Structured Data: A Study of the Passive Verb Alternation in French and Italiancited by 0
  10. 2026Bayesian Fine-tuning Yields Language Models that are as Bayesian as their Beliefs Allowcited by 0