#variational — Public Fediverse posts

'Entropic Gromov-Wasserstein Distances: Stability and Algorithms', by Gabriel Rioux, Ziv Goldfeld, Kengo Kato.

http://jmlr.org/papers/v25/24-0039.html

#regularization #wasserstein #variational

VC Roundup: Web3 funding hits $5.4B in 2024 - Blockchain-based startups raised $1.4 billion in the third quarter, brin... - https://cointelegraph.com/news/vc-roundup-web3-funding-5-4-billion-2024 #moonwalkfitness #borderless.xyz #venturecapital #variational #vixichain #karpatkey #startups #craftt #axal

Bayesian Meta-Learning Is All You Need

— Why is the deterministic view of meta-learning not sufficient?

— What is the variational inference?

— How can we design neural-based Bayesian meta-learning algorithms?

https://jameskle.com/writes/bayesian-meta-learning-is-all-you-need

#machinelearning #bayesian #metalearning #variational

'Structured Optimal Variational Inference for Dynamic Latent Space Models', by Peng Zhao, Anirban Bhattacharya, Debdeep Pati, Bani K. Mallick.

http://jmlr.org/papers/v25/22-0514.html

#variational #models #priors

'A Framework for Improving the Reliability of Black-box Variational Inference', by Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins.

http://jmlr.org/papers/v25/22-0327.html

#variational #adaptively #optimization

'A Framework for Improving the Reliability of Black-box Variational Inference', by Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins.

http://jmlr.org/papers/v25/22-0327.html

#variational #adaptively #optimization

'A Framework for Improving the Reliability of Black-box Variational Inference', by Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins.

http://jmlr.org/papers/v25/22-0327.html

#variational #adaptively #optimization

'A Framework for Improving the Reliability of Black-box Variational Inference', by Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins.

http://jmlr.org/papers/v25/22-0327.html

#variational #adaptively #optimization

'A Framework for Improving the Reliability of Black-box Variational Inference', by Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins.

http://jmlr.org/papers/v25/22-0327.html

#variational #adaptively #optimization

`Using the framework of utility-calibrated #variational inference, we unify Gaussian process approximation & data acquisition into a joint #optimization problem, thereby ensuring optimal decisions under a limited computational budget. Our approach can be used with any decision-theoretic acquisition function and is compatible with trust region methods like TuRBO... Our approach outperforms standard SVGPs on high-dimensional benchmark tasks in control and molecular design`

https://arxiv.org/abs/2406.04308

'A Variational Approach to Bayesian Phylogenetic Inference', by Cheng Zhang, Frederick A. Matsen IV.

http://jmlr.org/papers/v25/22-0348.html

#phylogenetic #bayesian #variational

'Low-rank Variational Bayes correction to the Laplace method', by Janet van Niekerk, Haavard Rue.

http://jmlr.org/papers/v25/21-1405.html

#variational #hyperparameters #approximations

'Additive smoothing error in backward variational inference for general state-space models', by Mathis Chagneux, Elisabeth Gassiat, Pierre Gloaguen, Sylvain Le Corff.

http://jmlr.org/papers/v25/22-1392.html

#variational #smoothing #estimation

'Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box', by Ryan Giordano, Martin Ingram, Tamara Broderick.

http://jmlr.org/papers/v25/23-1015.html

#variational #optimizer #optimizing

'Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family Observables', by Hamid Mousavi, Jakob Drefs, Florian Hirschberger, Jörg Lücke.

http://jmlr.org/papers/v24/22-0359.html

#probabilistic #sparse #variational

'Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics', by Kamélia Daudel, Joe Benton, Yuyang Shi, Arnaud Doucet.

http://jmlr.org/papers/v24/22-1160.html

#variational #divergence #estimators

Detecting incidental correlation in multimodal learning via latent variable modeling

Taro Makino, Yixin Wang, Krzysztof J. Geras, Kyunghyun Cho

Action editor: Thang Bui.

https://openreview.net/forum?id=QoRo9QmOAr

#multimodal #modality #variational

Detecting incidental correlation in multimodal learning via latent variable modeling

Taro Makino, Yixin Wang, Krzysztof J. Geras, Kyunghyun Cho

Action editor: Thang Bui.

https://openreview.net/forum?id=QoRo9QmOAr

#multimodal #modality #variational

Detecting incidental correlation in multimodal learning via latent variable modeling

Taro Makino, Yixin Wang, Krzysztof J. Geras, Kyunghyun Cho

Action editor: Thang Bui.

https://openreview.net/forum?id=QoRo9QmOAr

#multimodal #modality #variational

Detecting incidental correlation in multimodal learning via latent variable modeling

Taro Makino, Yixin Wang, Krzysztof J. Geras, Kyunghyun Cho

Action editor: Thang Bui.

https://openreview.net/forum?id=QoRo9QmOAr

#multimodal #modality #variational

Detecting incidental correlation in multimodal learning via latent variable modeling

Taro Makino, Yixin Wang, Krzysztof J. Geras, Kyunghyun Cho

Action editor: Thang Bui.

https://openreview.net/forum?id=QoRo9QmOAr

#multimodal #modality #variational

Variational Elliptical Processes

Maria Margareta Bånkestad, Jens Sjölund, Jalil Taghia, Thomas B. Schön

Action editor: Sinead Williamson.

https://openreview.net/forum?id=djN3TaqbdA

#gaussian #variational #likelihood

'Variational Inverting Network for Statistical Inverse Problems of Partial Differential Equations', by Junxiong Jia, Yanni Wu, Peijun Li, Deyu Meng.

http://jmlr.org/papers/v24/22-0006.html

#generative #bayesian #variational

'Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data', by Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann.

http://jmlr.org/papers/v24/21-1373.html

#variational #models #gibbs

'Variational Inference for Deblending Crowded Starfields', by Runjing Liu, Jon D. McAuliffe, Jeffrey Regier.

http://jmlr.org/papers/v24/21-0169.html

#galaxies #starnet #variational

'Variational Inference for Deblending Crowded Starfields', by Runjing Liu, Jon D. McAuliffe, Jeffrey Regier.

http://jmlr.org/papers/v24/21-0169.html

#galaxies #starnet #variational

'Variational Inference for Deblending Crowded Starfields', by Runjing Liu, Jon D. McAuliffe, Jeffrey Regier.

http://jmlr.org/papers/v24/21-0169.html

#galaxies #starnet #variational

'Variational Inference for Deblending Crowded Starfields', by Runjing Liu, Jon D. McAuliffe, Jeffrey Regier.

http://jmlr.org/papers/v24/21-0169.html

#galaxies #starnet #variational

'Variational Inference for Deblending Crowded Starfields', by Runjing Liu, Jon D. McAuliffe, Jeffrey Regier.

http://jmlr.org/papers/v24/21-0169.html

#galaxies #starnet #variational

Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport

Liyi Zhang, David Blei, Christian A Naesseth

Action editor: Michal Valko.

https://openreview.net/forum?id=7KW7zvKd7J

#transport #variational #adaptive

Is there THE language of fake news? Are #variational approaches useful for detecting #disinformation, in particular, in political #propaganda? Some thoughts inspired by the remarkable study by
Jack Grieve and Helena Woodfield in my new blog post: https://text-grinder.com/2023/08/05/is-there-the-language-of-disinformation/

A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models

Oleg Arenz, Philipp Dahlinger, Zihan Ye, Michael Volpp, Gerhard Neumann

Action editor: George Papamakarios.

https://openreview.net/forum?id=tLBjsX4tjs

#variational #mixture #gradient

'Monotonic Alpha-divergence Minimisation for Variational Inference', by Kamélia Daudel, Randal Douc, François Roueff.

http://jmlr.org/papers/v24/21-0249.html

#variational #divergence #multimodal

A Variational Perspective on Generative Flow Networks

Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent, Christian A Naesseth

https://openreview.net/forum?id=AZ4GobeSLq

#generative #flow #variational

Differentially private partitioned variational inference

Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop, Richard E Turner, Antti Honkela

https://openreview.net/forum?id=55BcghgicI

#privacy #private #variational

'On the geometry of Stein variational gradient descent', by Andrew Duncan, Nikolas Nüsken, Lukasz Szpruch.

http://jmlr.org/papers/v24/20-602.html

#stein #kernels #variational

U-Statistics for Importance-Weighted Variational Inference

Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon

https://openreview.net/forum?id=oXmwAPlbVw

#variational #batches #inference

U-Statistics for Importance-Weighted Variational Inference

Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon

https://openreview.net/forum?id=oXmwAPlbVw

#variational #batches #inference

U-Statistics for Importance-Weighted Variational Inference

Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon

https://openreview.net/forum?id=oXmwAPlbVw

#variational #batches #inference

U-Statistics for Importance-Weighted Variational Inference

Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon

https://openreview.net/forum?id=oXmwAPlbVw

#variational #batches #inference

U-Statistics for Importance-Weighted Variational Inference

Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon

https://openreview.net/forum?id=oXmwAPlbVw

#variational #batches #inference

'Discrete Variational Calculus for Accelerated Optimization', by Cédric M. Campos, Alejandro Mahillo, David Martín de Diego.

http://jmlr.org/papers/v24/21-1323.html

#variational #symplectic #optimization

#arxivfeed :

"GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions"
https://arxiv.org/abs/2206.05183

#MachineLearning #DeepLearning #Variational #Autoencoder #DynamicalSystems

#arxivfeed :

"Switching state-space modeling of neural signal dynamics"
https://www.biorxiv.org/content/10.1101/2022.11.18.517120v1

#Neuroscience #Neuro #Brain #Neuroimaging #EEG #StateSpaceModel #Inference #Variational #Bayesian #ExpectationMaximization