SOTAVerified

Variational Inference

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 851–900 of 2274 papers

TitleStatusHype
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference—0
Convergence Rates of Variational Inference in Sparse Deep Learning—0
Generalized Transformation-based Gradient—0
Generalized Variational Continual Learning—0
Bayesian Metric Learning for Robust Training of Deep Models under Noisy Labels—0
A Tutorial on Parametric Variational Inference—0
Generating Diverse Translation from Model Distribution with Dropout—0
Collapsed variational Bayes for Markov jump processes—0
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
Generative Flow Networks: Theory and Applications to Structure Learning—0
A Filtering Approach to Stochastic Variational Inference—0
Incorporating Word Correlation Knowledge into Topic Modeling—0
Generative Modeling of Neural Dynamics via Latent Stochastic Differential Equations—0
Generative Models for Learning from Crowds—0
Generative Particle Variational Inference via Estimation of Functional Gradients—0
A Nonparametric Bayesian Approach Toward Stacked Convolutional Independent Component Analysis—0
Generative Temporal Models with Memory—0
Combining Random Walks and Nonparametric Bayesian Topic Model for Community Detection—0
Generative Video Compression as Hierarchical Variational Inference—0
A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis—0
Geometric Dirichlet Means algorithm for topic inference—0
Geometric variational inference—0
Comparing the quality of neural network uncertainty estimates for classification problems—0
Bayesian Matrix Decomposition and Applications—0
Complementary Information Mutual Learning for Multimodality Medical Image Segmentation—0
GFlowOut: Dropout with Generative Flow Networks—0
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes—0
A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization—0
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models—0
'In-Between' Uncertainty in Bayesian Neural Networks—0
GRADE: Graph Dynamic Embedding—0
Gradient-based inference of abstract task representations for generalization in neural networks—0
A universal probabilistic spike count model reveals ongoing modulation of neural variability—0
Measuring Systematic Risk with Neural Network Factor Model—0
Incremental Variational Inference for Latent Dirichlet Allocation—0
Indian Buffet Process Deep Generative Models for Semi-Supervised Classification—0
Improving Graph Out-of-distribution Generalization on Real-world Data—0
Variational Laplace for Bayesian neural networks—0
Gradient Regularisation as Approximate Variational Inference—0
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference—0
AutoBayes: A Compositional Framework for Generalized Variational Inference—0
Computing with Categories in Machine Learning—0
PGODE: Towards High-quality System Dynamics Modeling—0
Differentially Private Continual Learning—0
Bayesian Learning to Optimize: Quantifying the Optimizer Uncertainty—0
Gray-box inference for structured Gaussian process models—0
Group Factor Analysis—0
A Non-negative VAE:the Generalized Gamma Belief Network—0
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes—0
Show:102550
← PrevPage 18 of 46Next →

No leaderboard results yet.