:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Rapakoulias, George, Pedram, Ali Reza, Liu, Fengjiao, Zhu, Lingjiong, Tsiotras, Panagiotis
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	https://arxiv.org/abs/2412.09059
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Steering Large Agent Populations using Mean-Field Schrodinger Bridges with Gaussian Mixture Models
par: Rapakoulias, George, et autres
Publié: (2025)

Stochastic Transition-Map Distillation for Fast Probabilistic Inference
par: Rapakoulias, George, et autres
Publié: (2026)

Schrodinger Bridges and Density Steering Problems for Gaussian Mixtures Models in Discrete-Time
par: Rapakoulias, George, et autres
Publié: (2026)

Optimal Covariance Steering for Discrete-Time Linear Stochastic Systems
par: Liu, Fengjiao, et autres
Publié: (2022)

Stochastic Control of UAVs: An Optimal Tradeoff between Performance, Flight Smoothness and Control Effort
par: Rapakoulias, George, et autres
Publié: (2024)

Discrete-Time Maximum Likelihood Neural Distribution Steering
par: Rapakoulias, George, et autres
Publié: (2024)

Nonlinear Stochastic Density Steering via Gaussian Mixture Schrodinger Bridges and Multiple Linearizations
par: Mosso, Mattia, et autres
Publié: (2026)

Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distances
par: Gao, Xuefeng, et autres
Publié: (2024)

Reachability and Controllability Analysis of the State Covariance for Linear Stochastic Systems
par: Liu, Fengjiao, et autres
Publié: (2024)

BaB-prob: Branch and Bound with Preactivation Splitting for Probabilistic Verification of Neural Networks
par: Wang, Fangji, et autres
Publié: (2025)

Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts?
par: Nguyen, Huy, et autres
Publié: (2024)

Structured Diffusion Models with Mixture of Gaussians as Prior Distribution
par: Jia, Nanshan, et autres
Publié: (2024)

Reachability Analysis of the State Transition and State Covariance Matrices for an LTV System
par: Liu, Fengjiao, et autres
Publié: (2026)

Communication-Aware Map Compression for Online Path-Planning: A Rate-Distortion Approach
par: Pedram, Ali Reza, et autres
Publié: (2025)

Communication-Aware Iterative Map Compression for Online Path-Planning
par: Psomiadis, Evangelos, et autres
Publié: (2025)

Accelerating Langevin Monte Carlo Sampling: A Large Deviations Analysis
par: Yao, Nian, et autres
Publié: (2025)

Privacy of SGD under Gaussian or Heavy-Tailed Noise: Guarantees without Gradient Clipping
par: Şimşekli, Umut, et autres
Publié: (2024)

Decentralized Proximal Stochastic Gradient Langevin Dynamics
par: Islam, Mohammad Rafiqul, et autres
Publié: (2026)

Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models
par: Gao, Xuefeng, et autres
Publié: (2023)

Sampling non-log-concave densities via Hessian-free high-resolution dynamics
par: Wang, Xiaoyu, et autres
Publié: (2026)

Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling
par: Gürbüzbalaban, Mert, et autres
Publié: (2022)

Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient
par: Lu, Weiguo, et autres
Publié: (2024)

Gaussian Mixture Flow Matching Models
par: Chen, Hansheng, et autres
Publié: (2025)

Gaussian Universality for Diffusion Models
par: Ghane, Reza, et autres
Publié: (2025)

Regime-Switching Langevin Monte Carlo Algorithms
par: Wang, Xiaoyu, et autres
Publié: (2025)

Fast Deep Mixtures of Gaussian Process Experts
par: Etienam, Clement, et autres
Publié: (2020)

uGMM-NN: Univariate Gaussian Mixture Model Neural Network
par: Ali, Zakeria Sharif
Publié: (2025)

Understanding Self-Supervised Learning via Gaussian Mixture Models
par: Bansal, Parikshit, et autres
Publié: (2024)

TreeFlow: Going beyond Tree-based Gaussian Probabilistic Regression
par: Wielopolski, Patryk, et autres
Publié: (2022)

Accelerating Constrained Sampling: A Large Deviations Approach
par: Wang, Yingli, et autres
Publié: (2025)

Learning Overspecified Gaussian Mixtures Exponentially Fast with the EM Algorithm
par: Assylbekov, Zhenisbek, et autres
Publié: (2025)

DIGing--SGLD: Decentralized and Scalable Langevin Sampling over Time--Varying Networks
par: Bajwa, Waheed U., et autres
Publié: (2025)

Learning Mixtures of Gaussians Using Diffusion Models
par: Gatmiry, Khashayar, et autres
Publié: (2024)

Universality of Gaussian-Mixture Reverse Kernels in Conditional Diffusion
par: Ishtiaque, Nafiz, et autres
Publié: (2026)

Intriguing Differences Between Zero-Shot and Systematic Evaluations of Vision-Language Transformer Models
par: Salman, Shaeke, et autres
Publié: (2024)

SAVER: A Toolbox for Sampling-Based, Probabilistic Verification of Neural Networks
par: Sivaramakrishnan, Vignesh, et autres
Publié: (2024)

Federated Gaussian Mixture Models
par: Pettersson, Sophia Zhang, et autres
Publié: (2025)

Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models
par: Wu, Yuchen, et autres
Publié: (2024)

Anchored Langevin Algorithms
par: Gurbuzbalaban, Mert, et autres
Publié: (2025)

Quadratic Gating Mixture of Experts: Statistical Insights into Self-Attention
par: Akbarian, Pedram, et autres
Publié: (2024)