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Variational Inference

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Showing 2001–2050 of 2274 papers

TitleStatusHype
An Equivalence between Bayesian Priors and Penalties in Variational Inference—0
IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data—0
Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images—0
Introducing Routing Uncertainty in Capsule Networks—0
Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification—0
ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks—0
ISA-VAE: Independent Subspace Analysis with Variational Autoencoders—0
Is MC Dropout Bayesian?—0
Isometric Gaussian Process Latent Variable Model for Dissimilarity Data—0
Mixture Representation Learning with Coupled Autoencoders—0
Joint Mapping and Calibration via Differentiable Sensor Fusion—0
Joint Modeling of Visual Objects and Relations for Scene Graph Generation—0
Joint Sparse Graph for Enhanced MIMO-AFDM Receiver Design—0
Joint State Estimation and Noise Identification Based on Variational Optimization—0
Kernel Implicit Variational Inference—0
Koopman-Equivariant Gaussian Processes—0
Kullback-Leibler Proximal Variational Inference—0
Langevin Diffusion Variational Inference—0
Langevin Dynamics as Nonparametric Variational Inference—0
Language-guided Skill Learning with Temporal Variational Inference—0
Large Language Bayes—0
Large-scale Heteroscedastic Regression via Gaussian Process—0
Large-Scale Joint Topic, Sentiment & User Preference Analysis for Online Reviews—0
Large Scale Tensor Regression using Kernels and Variational Inference—0
Latent Gaussian process with composite likelihoods and numerical quadrature—0
Latent Matters: Learning Deep State-Space Models—0
Latent mixed-effect models for high-dimensional longitudinal data—0
Latent Time Neural Ordinary Differential Equations—0
Latent Variables on Spheres for Sampling and Inference—0
LAVAE: Disentangling Location and Appearance—0
Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference—0
LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport—0
Learnable Distribution Calibration for Few-Shot Class-Incremental Learning—0
Learnable Explicit Density for Continuous Latent Space and Variational Inference—0
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks—0
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix—0
Learning and Inference in Imaginary Noise Models—0
Learning and Predicting Multimodal Vehicle Action Distributions in a Unified Probabilistic Model Without Labels—0
Learning an Embedding Space for Transferable Robot Skills—0
Learning an Explicit Weighting Scheme for Adapting Complex HSI Noise—0
Learning a Probabilistic Model for Diffeomorphic Registration—0
Learning a Small Mixture of Trees—0
Learning Causally-Generated Stationary Time Series—0
Learning Conditional Variational Autoencoders with Missing Covariates—0
Learning Deep Latent-variable MRFs with Amortized Bethe Free Energy Minimization—0
Learning Distributions over Permutations and Rankings with Factorized Representations—0
Learning Distributions via Monte-Carlo Marginalization—0
Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future—0
Learning from demonstration using products of experts: applications to manipulation and task prioritization—0
Learning From Unpaired Data: A Variational Bayes Approach—0
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