conftrace_

contrastive divergence

32 papers

Explore in graph

Also known as

CD

Co-occurring keywords

generative model (2892) energy-based model (251) restricted boltzmann machine (98) maximum likelihood (255) markov chain monte carlo (725) exponential family (124) partition function (81) parameter estimation (274) variational inference (2069) maximum likelihood estimation (233)

Papers

Preference Optimization via Contrastive Divergence: Your Policy Is Secretly an NLL Estimator AAAI 2026
Synergy of GFlowNet and Protein Language Model Makes a Diverse Antibody Designer AAAI 2025
On the connection between Noise-Contrastive Estimation and Contrastive Divergence AISTATS 2024
Near-Optimality of Contrastive Divergence Algorithms NIPS 2024
End-to-end Training of Deep Boltzmann Machines by Unbiased Contrastive Divergence with Local Mode Initialization ICML 2023
Learning Markov Random Fields for Combinatorial Structures via Sampling through Lovász Local Lemma AAAI 2023
XOR-CD: Linearly Convergent Constrained Structure Generation ICML 2021
Pseudo-Spherical Contrastive Divergence NIPS 2021
Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines NIPS 2021
Improved Contrastive Divergence Training of Energy-Based Models ICML 2021
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent NIPS 2021
Contrastive Divergence Learning with Chained Belief Propagation PGM 2020
Training Deep Energy-Based Models with f-Divergence Minimization ICML 2020
Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model CVPR 2019
Learning Generative ConvNets via Multi-Grid Modeling and Sampling CVPR 2018
Belief Propagation in Conditional RBMs for Structured Prediction AISTATS 2017
Learning RBM with a DC programming Approach ACML 2017
Learning Infinite RBMs with Frank-Wolfe NIPS 2016
Stochastic Neural Networks with Monotonic Activation Functions AISTATS 2016
A Theory of Generative ConvNet ICML 2016
Similarity-based Contrastive Divergence Methods for Energy-based Deep Learning Models ACML 2015
Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy NIPS 2015
Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations ICML 2015
Learning Heterogeneous Hidden Markov Random Fields AISTATS 2014
Interpretable Sparse High-Order Boltzmann Machines AISTATS 2014