# Towards Bayesian Deep Learning: A Framework and Some Existing Methods

arXiv: https://arxiv.org/abs/1608.06884 · HTML: https://mlsanity.com/paper/1608.06884/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2016.

## Built on by (most cited first, 30 of 58)

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- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics](https://mlsanity.com/paper/2106.02993/) (2021)
- [Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation](https://mlsanity.com/paper/2302.02561/) (2023)
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- [Auto-Parsing Network for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/2108.10568/) (2021)
- [Training-Free Bayesianization for Low-Rank Adapters of Large Language Models](https://mlsanity.com/paper/2412.05723/) (2024)
- [GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation](https://mlsanity.com/paper/2111.11293/) (2021)
- [Taxonomy-Structured Domain Adaptation](https://mlsanity.com/paper/2306.07874/) (2023)
- [Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Models](https://mlsanity.com/paper/2406.12649/) (2024)
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- [PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty](https://mlsanity.com/paper/2202.05063/) (2022)
- [Delving into Differentially Private Transformer](https://mlsanity.com/paper/2405.18194/) (2024)
- [Mitigating Reward Hacking in RLHF via Bayesian Non-negative Reward Modeling](https://mlsanity.com/paper/2602.10623/) (2026)
- [Context Uncertainty in Contextual Bandits with Applications to Recommender Systems](https://mlsanity.com/paper/2202.00805/) (2022)
- [Concept-Based Unsupervised Domain Adaptation](https://mlsanity.com/paper/2505.05195/) (2025)
- [Uncertainty-aware Self-training for Low-resource Neural Sequence Labeling](https://mlsanity.com/paper/2302.08659/) (2023)
- [Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding](https://mlsanity.com/paper/2310.13022/) (2023)
- [Towards An Efficient and Effective En Route Travel Time Estimation Framework](https://mlsanity.com/paper/2504.04086/) (2025)
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## Abstract (arXiv)

While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning and planning require an even higher level of intelligence. The past few years have seen major advances in many perception tasks using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, it is naturally desirable to tightly integrate deep learning and Bayesian models within a principled probabilistic framework, which we call Bayesian deep learning. In this unified framework, the perception of text or images using deep learning can boost the performance of higher-level inference and in return, the feedback from the inference process is able to enhance the perception of text or images. This paper proposes a general framework for Bayesian deep learning and reviews its recent applications on recommender systems, topic models, and control. In this paper, we also discuss the relationship and differences between Bayesian deep learning and other related topics like Bayesian treatment of neural networks.