Cover Image

Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionality

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lee, Kyeongwon, Lin, Lizhen, Park, Jaewoo, Jeong, Seonghyun
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces
by: Oh, Jeunghun, et al.
Published: (2026)

Posterior Contraction of Lévy Adaptive B-spline Regression in Besov Spaces
by: Oh, Jeunghun, et al.
Published: (2026)

Snowball: A Scalable All-to-All Ising Machine with Dual-Mode Markov Chain Monte Carlo Spin Selection and Asynchronous Spin Updates for Fast Combinatorial Optimization
by: Hong, Seungki, et al.
Published: (2026)

Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization
by: Yun, Taeyoung, et al.
Published: (2025)

Non-backtracking Graph Neural Networks
by: Park, Seonghyun, et al.
Published: (2023)

SplitQuant: Layer Splitting for Low-Bit Neural Network Quantization
by: Song, Jaewoo, et al.
Published: (2025)

Unsupervised Outlier Detection using Random Subspace and Subsampling Ensembles of Dirichlet Process Mixtures
by: Kim, Dongwook, et al.
Published: (2024)

Ensuring Functional Correctness of Large Code Models with Selective Generation
by: Jeong, Jaewoo, et al.
Published: (2025)

Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation
by: Sun, Shiyi, et al.
Published: (2026)

A Bayesian Convolutional Neural Network-based Generalized Linear Model
by: Jeon, Yeseul, et al.
Published: (2022)

Gibbs Sampling the Posterior of Neural Networks
by: Piccioli, Giovanni, et al.
Published: (2023)

Taming Gradient Oversmoothing and Expansion in Graph Neural Networks
by: Park, MoonJeong, et al.
Published: (2024)

Adaptive Replay Buffer for Offline-to-Online Reinforcement Learning
by: Song, Chihyeon, et al.
Published: (2025)

Network Distance Based on Laplacian Flows on Graphs
by: Bao, Dianbin, et al.
Published: (2018)

Functional Adjoint Sampler: Scalable Sampling on Infinite Dimensional Spaces
by: Park, Byoungwoo, et al.
Published: (2025)

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning
by: Lee, Giwon, et al.
Published: (2025)

Variational Inference: Posterior Threshold Improves Network Clustering Accuracy in Sparse Regimes
by: Li, Xuezhen, et al.
Published: (2023)

On the Interplay of Priors and Overparametrization in Bayesian Neural Network Posteriors
by: Kobialka, Julius, et al.
Published: (2026)

Intrinsic Lorentz Neural Network
by: Shi, Xianglong, et al.
Published: (2026)

Posterior Inference in Latent Space for Scalable Constrained Black-box Optimization
by: Om, Kiyoung, et al.
Published: (2025)

Asymptotic Error Bounds and Fractional-Bit Design for Fixed-Point Grover's Quantum Algorithm Emulation
by: Choi, Seonghyun, et al.
Published: (2025)

GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks
by: You, Jihee, et al.
Published: (2024)

Riemannian MeanFlow
by: Woo, Dongyeop, et al.
Published: (2026)

Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces
by: Siegel, Jonathan W.
Published: (2022)

Martingale Posterior Neural Networks for Fast Sequential Decision Making
by: Duran-Martin, Gerardo, et al.
Published: (2025)

Posterior Uncertainty Quantification in Neural Networks using Data Augmentation
by: Wu, Luhuan, et al.
Published: (2024)

On Cold Posteriors of Probabilistic Neural Networks: Understanding the Cold Posterior Effect and A New Way to Learn Cold Posteriors with Tight Generalization Guarantees
by: Zhang, Yijie
Published: (2024)

Minimax Adaptive Online Nonparametric Regression over Besov Spaces
by: Liautaud, Paul, et al.
Published: (2025)

Sparse Covariance Neural Networks
by: Cavallo, Andrea, et al.
Published: (2024)

Self-supervised Adversarial Purification for Graph Neural Networks
by: Lee, Woohyun, et al.
Published: (2026)

Forward and Inverse Simulation of Pseudo-Two-Dimensional Model of Lithium-Ion Batteries Using Neural Networks
by: Lee, Myeong-Su, et al.
Published: (2024)

SPDE Methods for Nonparametric Bayesian Posterior Contraction and Laplace Approximation
by: Alberola-Boloix, Enric, et al.
Published: (2026)

Guided Trajectory Generation with Diffusion Models for Offline Model-based Optimization
by: Yun, Taeyoung, et al.
Published: (2024)

GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning
by: Lee, Jaewoo, et al.
Published: (2024)

A Stein Gradient Descent Approach for Doubly Intractable Distributions
by: Lee, Heesang, et al.
Published: (2024)

On Measuring Intrinsic Causal Attributions in Deep Neural Networks
by: Saha, Saptarshi, et al.
Published: (2025)

Explaining Deep Neural Networks by Leveraging Intrinsic Methods
by: La Rosa, Biagio
Published: (2024)

Scalable Geometric Learning with Correlation-Based Functional Brain Networks
by: You, Kisung, et al.
Published: (2025)

Learning Collective Variables from BioEmu with Time-Lagged Generation
by: Park, Seonghyun, et al.
Published: (2025)

Temporal Graph Learning Recurrent Neural Network for Traffic Forecasting
by: Lee, Sanghyun, et al.
Published: (2024)