Awesome Statistical Mechanics

Gauge-Invariant, Parameter-Insensitive Regularization for Potential Recovery from Flow on Directed Graphs (2026)

Mohammad Forouhesh

5.88

Free energy landscape of Dense Associative Memory (2026)

Sumedha et al.

5.01

GNet: A scalable and flexible Gaussian process network with nonparametric neurons (2026)

Mengyang Gu

4.39

What Your Model Threw Away and Why You'll Want It Back: Masking, Fingerprinting, and Privacy from Discarded Geometry (2026)

Zachary P. Bradshaw

4.39

A Semiparametric Framework for Stochastic Fundamental Diagram Modeling (2026)

Pengnan Chi et al.

4.39

Learning Standard Model structure from LHC data with Riemannian flow matching (2026)

Midori Kato et al.

4.39

Nuclear Quantum Effects as a Denoising Problem (2026)

Weizhou Wang et al.

4.39

Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph (2026)

Duen Horng Chau et al.

2.00

Conformal Graph Prediction with Z-Gromov-Wasserstein Distances (2026)

Gabriel Melo et al.

1.78

Manifold Dimension Estimation via Local Graph Structure (2025)

Zelong Bi et al.

1.50

Scaling Non-Parametric Sampling with Representation (2025)

Vincent Lu et al.

1.50

Analysis of Semi-Supervised Learning on Hypergraphs (2025)

Adrien Weihs et al.

1.50

Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages (2016)

Yin Cheng Ng et al.

—

A Non-generative Framework and Convex Relaxations for Unsupervised Learning (2016)

Elad Hazan et al.

—

Categorical Reparameterization with Gumbel-Softmax (2016)

Eric Jang et al.

—

Sequential Local Learning for Latent Graphical Models (2017)

Sejun Park et al.

—

$k$-means as a variational EM approximation of Gaussian mixture models (2017)

J\"org L\"ucke et al.

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Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment (2017)

Ruben H\"uhnerbein et al.

—

Convolutional Normalizing Flows (2017)

Guoqing Zheng et al.

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Learning Causally-Generated Stationary Time Series (2018)

Wessel Bruinsma and Richard E. Turner

—

Sylvester Normalizing Flows for Variational Inference (2018)

Rianne van den Berg and Leonard Hasenclever and Jakub M. Tomczak and Max Welling

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Structured Bayesian Gaussian process latent variable model (2018)

Steven Atkinson and Nicholas Zabaras

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VFunc: a Deep Generative Model for Functions (2018)

Philip Bachman et al.

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DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures (2018)

Andrew R. Lawrence and Carl Henrik Ek and Neill D. F. Campbell

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Semi-unsupervised Learning of Human Activity using Deep Generative Models (2018)

Matthew Willetts et al.

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On the Limitations of Representing Functions on Sets (2019)

Edward Wagstaff et al.

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Understanding MCMC Dynamics as Flows on the Wasserstein Space (2019)

Chang Liu et al.

—

Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data (2019)

Clement Abi Nader et al.

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Generative models as parsimonious descriptions of sensorimotor loops (2019)

Manuel Baltieri et al.

—

Fast Flow Reconstruction via Robust Invertible nxn Convolution (2019)

Thanh-Dat Truong et al.

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Neural Spline Flows (2019)

Conor Durkan et al.

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Latent Gaussian process with composite likelihoods and numerical quadrature (2019)

Siddharth Ramchandran et al.

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Compositional uncertainty in deep Gaussian processes (2019)

Ivan Ustyuzhaninov et al.

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Exact asymptotics for phase retrieval and compressed sensing with random generative priors (2019)

Benjamin Aubin et al.

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Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax (2019)

Andres Potapczynski et al.

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Invertible Generative Modeling using Linear Rational Splines (2020)

Hadi M. Dolatabadi and Sarah Erfani and Christopher Leckie

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Variational Wasserstein Barycenters for Geometric Clustering (2020)

Liang Mi

—

MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference (2020)

Achille Thin et al.

—

TraLFM: Latent Factor Modeling of Traffic Trajectory Data (2020)

Meng Chen et al.

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The Inverse G-Wishart Distribution and Variational Message Passing (2020)

L. Maestrini and M.P. Wand

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Ordering Dimensions with Nested Dropout Normalizing Flows (2020)

Artur Bekasov et al.

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Isometric Gaussian Process Latent Variable Model for Dissimilarity Data (2020)

Martin J{\o}rgensen and S{\o}ren Hauberg

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Reliable Categorical Variational Inference with Mixture of Discrete Normalizing Flows (2020)

Tomasz Ku\'smierczyk et al.

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Generative Flows with Matrix Exponential (2020)

Changyi Xiao et al.

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Spacetime Autoencoders Using Local Causal States (2020)

Adam Rupe and James P. Crutchfield

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Learning Exponential Family Graphical Models with Latent Variables using Regularized Conditional Likelihood (2020)

Armeen Taeb et al.

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Transforming Gaussian Processes With Normalizing Flows (2020)

Juan Maro\~nas et al.

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Evaluating Disentanglement of Structured Representations (2021)

Rapha\"el Dang-Nhu

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Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement (2021)

Andrew Slavin Ross and Finale Doshi-Velez

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Generative Archimedean Copulas (2021)

Yuting Ng et al.

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A prior-based approximate latent Riemannian metric (2021)

Georgios Arvanitidis et al.

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Universal Approximation of Residual Flows in Maximum Mean Discrepancy (2021)

Zhifeng Kong et al.

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Generative Locally Linear Embedding (2021)

Benyamin Ghojogh et al.

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Convolutional Normalizing Flows for Deep Gaussian Processes (2021)

Haibin Yu et al.

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Geometric convergence of elliptical slice sampling (2021)

Viacheslav Natarovskii et al.

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E(n) Equivariant Normalizing Flows (2021)

Victor Garcia Satorras et al.

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Pulling back information geometry (2021)

Georgios Arvanitidis et al.

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CovarianceNet: Conditional Generative Model for Correct Covariance Prediction in Human Motion Prediction (2021)

Aleksey Postnikov et al.

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A moment-matching metric for latent variable generative models (2021)

C\'edric Beaulac

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A first approach to closeness distributions (2021)

Jesus Cerquides

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Awesome Statistical Mechanics

Datasets & benchmarks

Key papers

Statistical Mechanics

Awesome Statistical Mechanics

Datasets & benchmarks

Key papers