#variationalinference search results

🙌New #SpecialIssue "Statistical Inference: Theory and Methods" edited by Prof. Michel Broniatowski, is Open for Submission! ➡️Submit to the Special Issue: mdpi.com/journal/entrop… 📅Submission deadline: 31 October 2025 #VariationalInference #BayesianInference #MachineLearning

"Navigating uncertainty in an efficient way: the negative of ELBO is known as variational free energy in neuroscience." Artem Kirsanov Explore how Variational Inference and ELBO enable intelligent systems to model probability distributions tractably. #VariationalInference…

📯 Check out the new publication! Sparse #Bayesian #NeuralNetworks: Bridging Model and Parameter Uncertainty through Scalable #VariationalInference buff.ly/4axVFv3 #BNNs #modelselection #predictiveuncertainty #MDPIOpenAccess @ComSciMath_Mdpi

(2/6) In #VariationalInference & #GenerativeModeling, minimizing f-divergences is key (e.g. #KL, or Jensen-Shannon divergence in Minimax-#GAN). Since f-divergence between measures with disjoint support is infty, we regularize with a squared MMD induced by a characteristic kernel.

Careless, a program for scaling diffraction data using the Bayesian optimization method variational inference, is introduced and is contrasted with historical scaling algorithms @harvard @IUCr #XRayCrystallography #Scaling #VariationalInference doi.org/10.1107/S20597…

New episode is out! All about #MCMC & #VariationalInference Enjoy, and get in touch for any feedback or suggestions ;)

Luis A. Aldama et al.: Correcting systematic errors in diffraction data with modern scaling algorithms #XRayCrystallography #Scaling #VariationalInference @harvard... #IUCr scripts.iucr.org/cgi-bin/paper?…

RT Variational Inference: The Basics #ai #machinelearning #variationalinference #bayesianstatistics dlvr.it/Sqmqqm

For the #bayesian community, here a stack exchange post where clarifications are needed on variational bayes: stats.stackexchange.com/questions/6171… Any insights are welcomed #variationalinference

Our recent paper on imputing missing values using deep Gaussian processes (MGP) is online now. "Gaussian processes for missing value imputation" Knowledge-Based Systems sciencedirect.com/science/articl… #gaussianprocess #deeplearning #variationalinference #data

RT An intuitive comparison of MCMC and Variational Inference dlvr.it/Sf1RvQ #machinelearning #variationalinference #bayesianstatistics

For #machinelearning enthusiasts, a specially structured #variationalinference is used with cell state proportions as auxiliary variables ➕ a variational information bottleneck approach (PoE) for histology integration 💻 6/

deriving coordinate ascent variational inference (CAVI) updates is truly a pain in the ass... #GraphicalModels #VariationalInference #MachineLearning

#variationalinference #technology #artificialintelligence Columbia U’s Infinitely Deep Probabilistic Model Adapts Its Complexity to the Data at Hand dlvr.it/SZ2n7M

Looking forward to attending (and speaking at) a workshop on Monte Carlo methods and approximate dynamic programming (variational inference) in Paris Aug. 25-26! #MonteCarlo #VariationalInference #Bayes #Statistics #DeepLearning #MachineLearning workshop-on-monte-carlo2022.essec.edu/welcome

Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection Bhatia et al.: arxiv.org/abs/2207.11208 #Artificialintelligence #DeepLearning #VariationalInference

Bayesian Gaussian mixtures provide an amazing example of how hard integrals can become in #MachineLearning and why we would need #VariationalInference. Have a look at this 🧵👇 #artificalintelligence #AI #DataScience #DataScientist #Computertechnology #informationtechnology

#VariationalInference (VI) is an approximate inference technique that solves the core problems of modern statistics and provides solutions to other inference techniques’ shortcomings. This paper is a great starter to dig into Variation Inference. link.medium.com/LKkOUQgB3lb

new blog post --- how noisy are different ELBO gradient estimators? #VariationalInference #MachineLearning andymiller.github.io/2016/12/19/elb…

Nested Variational Inference Zimmermann et al.: arxiv.org/abs/2106.11302 #ArtificialIntelligence #DeepLearning #VariationalInference

#mdpientropy Gaussian Mean Field Regularizes by Limiting Learned Information mdpi.com/1099-4300/21/8… #informationtheory #variationalinference #machinelearning

new post: reducing reparameterization gradient variance (paper+code+cat gif) #VariationalInference, #MachineLearning andymiller.github.io/2017/05/23/rv-…

Bayesian Gaussian mixtures provide an amazing example of how hard integrals can become in #MachineLearning and why we would need #VariationalInference. Have a look at this 🧵👇 #artificalintelligence #AI #DataScience #DataScientist #Computertechnology #informationtechnology

Exercise: compute the update rule for the factor analysis model #variationalinference #probabilisticai

Looking forward to attending (and speaking at) a workshop on Monte Carlo methods and approximate dynamic programming (variational inference) in Paris Aug. 25-26! #MonteCarlo #VariationalInference #Bayes #Statistics #DeepLearning #MachineLearning workshop-on-monte-carlo2022.essec.edu/welcome

Pathfinder: Parallel quasi-Newton variational inference Zhang et al.: arxiv.org/abs/2108.03782 #ArtificialIntelligence #DeepLearning #VariationalInference

#mdpientropy "Sampling the Variational Posterior with Local Refinement" mdpi.com/1099-4300/23/1… #bayesianinference #variationalinference #deepneuralnetworks #contextualbandits

For #machinelearning enthusiasts, a specially structured #variationalinference is used with cell state proportions as auxiliary variables ➕ a variational information bottleneck approach (PoE) for histology integration 💻 6/

Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection Bhatia et al.: arxiv.org/abs/2207.11208 #Artificialintelligence #DeepLearning #VariationalInference

#mdpientropy "Probabilistic Models with Deep Neural Networks" mdpi.com/1099-4300/23/1… deep #probabilisticmodeling #variationalinference #neuralnetworks #latentvariablemodels #Bayesianlearning

Careless, a program for scaling diffraction data using the Bayesian optimization method variational inference, is introduced and is contrasted with historical scaling algorithms @harvard @IUCr #XRayCrystallography #Scaling #VariationalInference doi.org/10.1107/S20597…

Fitting a Bayesian #neuralnetwork in PyMC3 with #variationalinference bit.ly/2aux56L

Combining #VariationalInference with #DeepLearning at #MachineLearning coffee seminar @AaltoUniversity by Arto Klami

Using full conditionals, one can get a coordinate ascent scheme for #VariationalInference, leading to convergence in exponential time (proof still in work). One can also get an efficient #Gibbssampling algorithm with outstanding efficiency.

The foundation of many an application in today's AI/ML algorithms has its base in this GEM ( General E-M ) algorithm published in 1977. Glad to have it in my Bibtex references. #VariationalInference #ML

RT On Distribution of Z’s in VAE dlvr.it/RhqPTm #variationalautoencoder #variationalinference #kldivergence

RT Variational Methods in Deep Learning dlvr.it/RnHvGx #machinelearning #deeplearning #variationalinference

RT Variational Inference: The Basics #ai #machinelearning #variationalinference #bayesianstatistics dlvr.it/Sqmqqm