Probabilistic Generative Model

Probabilistic generative models aim to learn the underlying probability distribution of data to generate new, similar data samples. Current research emphasizes improving the efficiency and accuracy of these models, focusing on architectures like variational autoencoders, diffusion models, and generative flow networks, as well as advanced inference techniques such as variational inference and Markov Chain Monte Carlo methods. These models find applications across diverse fields, including natural language processing, image synthesis, drug discovery, and social science modeling, offering powerful tools for data analysis and generation in various domains. Challenges remain in addressing issues like distribution mismatch in datasets and efficiently handling high-dimensional data.

Papers

May 24, 2022

May 9, 2022

A Probabilistic Generative Model of Free Categories
Eli Sennesh, Tom Xu, Yoshihiro Maruyama
Domain Specific Probabilistic Generative Model Category Theory New Category

April 28, 2022

Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor
Yang Yang, Zhiying Cui, Junjie Xu, Changhong Zhong, Wei-Shi Zheng, Ruixuan Wang
Continual LEArning Class Incremental Learning Probabilistic Generative Model Bayesian Model Pre Trained Feature Extractor

April 25, 2022

Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials
Lai Wei, Qinyang Li, Yuqi Song, Stanislav Stefanov, Edirisuriya M. D. Siriwardane, Fanglin Chen, Jianjun Hu
Language Model Generative Question Material Response Probabilistic Generative Model Transformer Based LLM Material Informatics Material Language

March 30, 2022

Learning Fair Models without Sensitive Attributes: A Generative Approach
Huaisheng Zhu, Enyan Dai, Hui Liu, Suhang Wang
Generative Approach Probabilistic Generative Model Sensitive Attribute Fair Model FAir Classifier

March 22, 2022

Representation Uncertainty in Self-Supervised Learning as Variational Inference
Hiroki Nakamura, Masashi Okada, Tadahiro Taniguchi
Self Supervised Learning Variational Inference Variational Autoencoder Unsupervised Representation Learning Probabilistic Generative Model Uncertainty Representation

March 15, 2022

Generative models and Bayesian inversion using Laplace approximation
Manuel Marschall, Gerd Wübbeler, Franko Schmähling, Clemens Elster
Generative Model Probabilistic Generative Model Bayesian Inverse Problem Bayesian Inversion Laplace Approximation Prior Density

February 24, 2022

Estimators of Entropy and Information via Inference in Probabilistic Models
Feras A. Saad, Marco Cusumano-Towner, Vikash K. Mansinghka
Scientific Inference Variational Inference Mutual Information Probabilistic Model Full Information Information Theoretic Linear Estimator Probabilistic Generative Model Stable Entropy

November 30, 2021

The Exponentially Tilted Gaussian Prior for Variational Autoencoders
Griffin Floto, Stefan Kremer, Mihai Nica
Variational Autoencoder Variational Autoencoders RSD Difference of Gaussian Probabilistic Generative Model Prior Distribution