mlsanityData to 7 Oct 2026

DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors

arXiv 1805.07445 ↗First posted 18 May 2018stat.ML cs.LGcited by 19

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Abstract (arXiv)

Boltzmann machines are powerful distributions that have been shown to be an effective prior over binary latent variables in variational autoencoders (VAEs). However, previous methods for training discrete VAEs have used the evidence lower bound and not the tighter importance-weighted bound. We propose two approaches for relaxing Boltzmann machines to continuous distributions that permit training with importance-weighted bounds. These relaxations are based on generalized overlapping transformations and the Gaussian integral trick. Experiments on the MNIST and OMNIGLOT datasets show that these relaxations outperform previous discrete VAEs with Boltzmann priors. An implementation which reproduces these results is available at https://github.com/QuadrantAI/dvae .

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 May 2018.

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Citing papers per year: 2021: 5; 2022: 1; 2023: 4; 2024: 4; 2025: 4; 2026: 1.

19 papers.

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  1. 2021Score-based Generative Modeling in Latent Spacecited by 634
  2. 2023GIVT: Generative Infinite-Vocabulary Transformerscited by 80
  3. 2021Controllable and Compositional Generation with Latent-Space Energy-Based Modelscited by 64
  4. 2021Discrete Representations Strengthen Vision Transformer Robustnesscited by 34
  5. 2022Lossy Image Compression with Quantized Hierarchical VAEscited by 32
  6. 2021Classical Planning in Deep Latent Spacecited by 21
  7. 2024DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latentscited by 20
  8. 2023Lewis's Signaling Game as beta-VAE For Natural Word Lengths and Segmentscited by 11
  9. 2024On the Role of Discrete Tokenization in Visual Representation Learningcited by 7
  10. 2025MoFM: A Large-Scale Human Motion Foundation Modelcited by 5
  11. 2024Diffusion Beats Autoregressive: An Evaluation of Compositional Generation in Text-to-Image Modelscited by 4
  12. 2021Discrete Auto-regressive Variational Attention Models for Text Modelingcited by 2
7 more
  1. 2023Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classificationcited by 2
  2. 2025VTD-CLIP: Video-to-Text Discretization via Prompting CLIPcited by 2
  3. 2023Anomaly Detection in Aeronautics Data with Quantum-compatible Discrete Deep Generative Modelcited by 1
  4. 2025Zero-Variance Gradients for Variational Autoencoderscited by 1
  5. 2026EXCODER: EXplainable Classification Of DiscretE time series Representationscited by 1
  6. 2024Improved Variational Inference in Discrete VAEs using Error Correcting Codescited by 0
  7. 2025Point Cloud Quantization through Multimodal Prompting for 3D Understandingcited by 0