Papers Variational Inference
“Variational Inference” 태그가 달린 논문 2,274편 · 필터 해제
Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Tensor Network (TN) Kernel Machines speed up model learning by representing parameters as low-rank TNs, reducing computation and memory use. However, most TN-based Kernel methods are deterministic and ignore parameter un…
feature selectionUncertainty QuantificationVariational InferenceScalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference
Despite their widespread use, large language models (LLMs) are known to hallucinate incorrect information and be poorly calibrated. This makes the uncertainty quantification of these models of critical importance, especi…
Bayesian InferenceUncertainty QuantificationVariational InferenceVHU-Net: Variational Hadamard U-Net for Body MRI Bias Field Correction
Bias field artifacts in magnetic resonance imaging (MRI) scans introduce spatially smooth intensity inhomogeneities that degrade image quality and hinder downstream analysis. To address this challenge, we propose a novel…
Computational EfficiencyDecoderVariational InferenceBranching Stein Variational Gradient Descent for sampling multimodal distributions
We propose a novel particle-based variational inference method designed to work with multimodal distributions. Our approach, referred to as Branched Stein Variational Gradient Descent (BSVGD), extends the classical Stein…
Variational InferenceRobust Recursive Fusion of Multiresolution Multispectral Images with Location-Aware Neural Networks
Multiresolution image fusion is a key problem for real-time satellite imaging and plays a central role in detecting and monitoring natural phenomena such as floods. It aims to solve the trade-off between temporal and spa…
Variational InferenceVariational Inference with Mixtures of Isotropic Gaussians
Variational inference (VI) is a popular approach in Bayesian inference, that looks for the best approximation of the posterior distribution within a parametric family, minimizing a loss that is typically the (reverse) Ku…
Bayesian InferenceVariational InferenceRobust Filtering -- Novel Statistical Learning and Inference Algorithms with Applications
State estimation or filtering serves as a fundamental task to enable intelligent decision-making in applications such as autonomous vehicles, robotics, healthcare monitoring, smart grids, intelligent transportation, and …
Autonomous VehiclesBayesian InferenceIndoor LocalizationPoint Cloud Registration+2Bayesian Probabilistic Matrix Factorization
Matrix factorization is a widely used technique in recommendation systems. Probabilistic Matrix Factorization (PMF) [1] extends traditional matrix factorization by incorporating probability distributions over latent fact…
Bayesian InferenceComputational EfficiencyRecommendation SystemsUncertainty Quantification+1Variational Inference Optimized Using the Curved Geometry of Coupled Free Energy
We introduce an optimization framework for variational inference based on the coupled free energy, extending variational inference techniques to account for the curved geometry of the coupled exponential family. This fam…
Variational InferenceRao-Blackwellised Reparameterisation Gradients
Latent Gaussian variables have been popularised in probabilistic machine learning. In turn, gradient estimators are the machinery that facilitates gradient-based optimisation for models with latent Gaussian variables. Th…
Variational InferenceBayesian Learning for Domain-Invariant Speaker Verification and Anti-Spoofing
The performance of automatic speaker verification (ASV) and anti-spoofing drops seriously under real-world domain mismatch conditions. The relaxed instance frequency-wise normalization (RFN), which normalizes the frequen…
Speaker VerificationVariational InferenceStability of Mean-Field Variational Inference
Mean-field variational inference (MFVI) is a widely used method for approximating high-dimensional probability distributions by product measures. This paper studies the stability properties of the mean-field approximatio…
Bayesian InferenceVariational InferenceVariational Supervised Contrastive Learning
Contrastive learning has proven to be highly efficient and adaptable in shaping representation spaces across diverse modalities by pulling similar samples together and pushing dissimilar ones apart. However, two key limi…
Contrastive LearningFew-Shot LearningTransfer LearningVariational InferenceHalf-AVAE: Adversarial-Enhanced Factorized and Structured Encoder-Free VAE for Underdetermined Independent Component Analysis
This study advances the Variational Autoencoder (VAE) framework by addressing challenges in Independent Component Analysis (ICA) under both determined and underdetermined conditions, focusing on enhancing the independenc…
Causal InferenceDecoderDisentanglementVariational InferenceVariational Inference for Quantum HyperNetworks
Binary Neural Networks (BiNNs), which employ single-bit precision weights, have emerged as a promising solution to reduce memory usage and power consumption while maintaining competitive performance in large-scale system…
Bayesian InferenceVariational InferenceAmortized variational transdimensional inference
The expressiveness of flow-based models combined with stochastic variational inference (SVI) has, in recent years, expanded the application of optimization-based Bayesian inference to include problems with complex data r…
Bayesian InferenceBayesian OptimizationVariational InferenceSemi-Implicit Variational Inference via Kernelized Path Gradient Descent
Semi-implicit variational inference (SIVI) is a powerful framework for approximating complex posterior distributions, but training with the Kullback-Leibler (KL) divergence can be challenging due to high variance and bia…
Variational InferenceMudiNet: Task-guided Disentangled Representation Learning for 5G Indoor Multipath-assisted Positioning
In the fifth-generation communication system (5G), multipath-assisted positioning (MAP) has emerged as a promising approach. With the enhancement of signal resolution, multipath component (MPC) are no longer regarded as …
Representation LearningVariational InferenceSelf-supervised Latent Space Optimization with Nebula Variational Coding
Deep learning approaches process data in a layer-by-layer way with intermediate (or latent) features. We aim at designing a general solution to optimize the latent manifolds to improve the performance on classification, …
FormMetric LearningVariational InferenceVariational Adaptive Noise and Dropout towards Stable Recurrent Neural Networks
This paper proposes a novel stable learning theory for recurrent neural networks (RNNs), so-called variational adaptive noise and dropout (VAND). As stabilizing factors for RNNs, noise and dropout on the internal state o…
Imitation LearningLearning TheoryVariational Inference