Please join us next Wednesday (9.13) at 1:30 p.m. CT for a virtual seminar on robust unsupervised multi-task and transfer learning on Gaussian mixture models by Dr. Yang Feng, associate professor of biostatistics at NYU School of Global Public Health. Contact Cierra.Streeter[at]vumc.org for access. #biostatistics #gaussian
https://biostat.app.vumc.org/wiki/Main/YangFeng09132023
Variational Elliptical Processes
Maria Margareta Bånkestad, Jens Sjölund, Jalil Taghia, Thomas B. Schön
Action editor: Sinead Williamson.
#gaussian #variational #likelihood
3D Gaussian Splatting goes dynamic in "Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis".
They model dynamic scenes by allowing their 3D Gaussians to move and rotate over time while enforcing that they have persistent color, opacity, and size.
#nerf #computervision #computergraphics #3dvision #gaussian
Non-asymptotic approximations of Gaussian neural networks via second-order Poincar\'e inequalities
#gaussian #asymptotic #approximations
Do you want to know how to fit a probability distribution to data? Watch the latest LabPlot's video tutorial.
https://www.youtube.com/watch?v=g0OQrAVwsTw
#DistributionFitting #ProbabilityDistribution #Distribution #StatisticalDistribution #Gaussian #Log-normal #Probability #Poisson #Binomial #Exponential #MaximumLikelihood #LabPlot
#labplot #maximumlikelihood #exponential #binomial #poisson #Probability #log #gaussian #statisticaldistribution #distribution #probabilitydistribution #distributionfitting
On Average-Case Error Bounds for Kernel-Based Bayesian Quadrature
Xu Cai, Thanh Lam, Jonathan Scarlett
Action editor: Nishant Mehta.
#gaussian #quadrature #kernels
A probabilistic Taylor expansion with Gaussian processes
Learning Interpolations between Boltzmann Densities
Bálint Máté, François Fleuret
Action editor: Ruoyu Sun.
#Boltzmann #gaussian #interpolations
Non-asymptotic approximations of Gaussian neural networks via second-order Poincar\'e inequalities
#gaussian #approximations #approximation
Generalized Information Bottleneck for Gaussian Variables
#correlated #gaussian #correlation
'Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics', by Mu Niu, Zhenwen Dai, Pokman Cheung, Yizhu Wang.
http://jmlr.org/papers/v24/22-0627.html
#gaussian #manifolds #manifold
#gaussian #manifolds #manifold
'Gaussian Processes with Errors in Variables: Theory and Computation', by Shuang Zhou, Debdeep Pati, Tianying Wang, Yun Yang, Raymond J. Carroll.
http://jmlr.org/papers/v24/21-1480.html
#bayesian #nonparametric #gaussian
#bayesian #nonparametric #gaussian
'Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees', by William J. Wilkinson, Simo Särkkä, Arno Solin.
http://jmlr.org/papers/v24/21-1298.html
#gaussian #bayesian #posterior
#gaussian #bayesian #posterior
'Posterior Contraction for Deep Gaussian Process Priors', by Gianluca Finocchio, Johannes Schmidt-Hieber.
http://jmlr.org/papers/v24/21-0556.html
#priors #posterior #gaussian
Bayesian Transformed Gaussian Processes
Xinran Zhu, Leo Huang, Eric Hans Lee, Cameron Alexander Ibrahim, David Bindel
Now (7pm ET Wed) watch https://youtu.be/QS0VmCD9YLU(FEEL FREE TO SUBSCRIBE TO YOUTUBE
@hajiaghayi
FOR FUTURE LESSONS) Lesson 14: Introduction to Algorithms by Mohammad Hajiaghayi: We talk about #Probability (Part 2) useful for designing #randomized #algorithms
#algorithms, #design, #induction, #recursive, #randomizedalgorithms, #probability, #randominput ,
#probabilitytheory, #randomvariables, #expectations, #variance, #Bernoulli, #Binomial, #Poisson, #Normaldistribution, #Gaussian, #Python, #numpy.random, #scipy.stats, #correlation, #Pearson, #spearman, #geeksforgeeks , #hackerrank, #leetcode, #cs, #computerscience
#computerscience #cs #leetcode #hackerrank #geeksforgeeks #spearman #pearson #correlation #scipy #numpy #python #gaussian #normaldistribution #poisson #binomial #Bernoulli #variance #expectations #randomvariables #probabilitytheory #randominput #randomizedalgorithms #recursive #induction #design #algorithms #randomized #probability
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes
Magnus Ross, Markus Heinonen
#hamiltonian #trajectories #gaussian
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems
Andreas Look, Barbara Rakitsch, Melih Kandemir, Jan Peters
#Multimodal #benchmark #gaussian
Bayesian Gaussian Mixture Models are a great way to improve the clustering performance of real world datasets.
Find out why and where you might want to use them, and how to implement them efficiently.
#clustering #DataScience #MachineLearning #Bayesian #Gaussian #MixtureModels #BGMM #GMM #unsupervisedlearning
#clustering #datascience #machinelearning #bayesian #gaussian #mixturemodels #bgmm #gmm #unsupervisedlearning
On Average-Case Error Bounds for Kernel-Based Bayesian Quadrature
#gaussian #quadrature #kernels