Imagen de Portada

MEP-Net: Generating Solutions to Scientific Problems with Limited Knowledge by Maximum Entropy Principle

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Yang, Wuyue, Peng, Liangrong, Li, Guojie, Hong, Liu
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Data Analysis, Statistics and Probability
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Bayesian Hierarchical Models and the Maximum Entropy Principle
por: Brewer, Brendon J.
Publicado: (2026)

CausationEntropy: Pythonic Optimal Causation Entropy
por: Slote, Kevin, et al.
Publicado: (2026)

Scientific Data Compression and Super-Resolution Sampling
por: Vu, Minh, et al.
Publicado: (2025)

Exploring the Limitations of kNN Noisy Feature Detection and Recovery for Self-Driving Labs
por: Shi, Qiuyu, et al.
Publicado: (2025)

Limits to classification performance by relating Kullback-Leibler divergence to Cohen's Kappa
por: Crow, L., et al.
Publicado: (2024)

Intrinsic Signal Models Defined by the High-Dimensional, Small-Sample Limit
por: Mototake, Yoh-ichi, et al.
Publicado: (2023)

Information-theoretic Quantification of High-order Feature Effects in Classification Problems
por: Lazic, Ivan, et al.
Publicado: (2025)

Detail Across Scales: Multi-Scale Enhancement for Full Spectrum Neural Representations
por: Ni, Yuan, et al.
Publicado: (2025)

Integrating Bayesian Spectral Deconvolution and Expert Scientific Reasoning for Robust Peak Estimation
por: Okubo, Hayato, et al.
Publicado: (2026)

Response Theory via Generative Score Modeling
por: Giorgini, Ludovico Theo, et al.
Publicado: (2024)

Maximum Information Extraction Via Clustering and Minimization of Shannon Entropy
por: Becchi, Matteo, et al.
Publicado: (2025)

Scientific machine learning in Hydrology: a unified perspective
por: Adombi, Adoubi Vincent De Paul
Publicado: (2025)

Probabilistic Flux Limiters
por: Nguyen-Fotiadis, Nga T. T., et al.
Publicado: (2024)

Tunable correlation retention: A statistical method for generating synthetic data
por: Jävergård, Nicklas, et al.
Publicado: (2024)

DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection
por: Mei, Lin-Feng, et al.
Publicado: (2024)

Statistical limits of correlation detection in trees
por: Ganassali, Luca, et al.
Publicado: (2022)

OASIS: A Deep Learning Framework for Universal Spectroscopic Analysis Driven by Novel Loss Functions
por: Young, Chris, et al.
Publicado: (2025)

Lines of Thought in Large Language Models
por: Sarfati, Raphaël, et al.
Publicado: (2024)

Comparative Analysis of Machine Learning-Based Imputation Techniques for Air Quality Datasets with High Missing Data Rates
por: Yan, Sen, et al.
Publicado: (2024)

Principled model selection for stochastic dynamics
por: Gerardos, Andonis, et al.
Publicado: (2025)

GeoConformal prediction: a model-agnostic framework of measuring the uncertainty of spatial prediction
por: Lou, Xiayin, et al.
Publicado: (2024)

AI Meets Antimatter: Unveiling Antihydrogen Annihilations
por: Ferreira, Ashley, et al.
Publicado: (2024)

GAMMA_FLOW: Guided Analysis of Multi-label spectra by MAtrix Factorization for Lightweight Operational Workflows
por: Rädle, Viola, et al.
Publicado: (2025)

Position: Solve Layerwise Linear Models First to Understand Neural Dynamical Phenomena (Neural Collapse, Emergence, Lazy/Rich Regime, and Grokking)
por: Nam, Yoonsoo, et al.
Publicado: (2025)

Learning effective good variables from physical data
por: Barletta, Giulio, et al.
Publicado: (2024)

Development of a high-resolution indoor radon map using a new machine learning-based probabilistic model and German radon survey data
por: Petermann, Eric, et al.
Publicado: (2023)

Reliable Statistical Guarantees for Conformal Predictors with Small Datasets
por: Sánchez-Domínguez, Miguel, et al.
Publicado: (2025)

Scalable Sparse Regression for Model Discovery: The Fast Lane to Insight
por: Golden, Matthew
Publicado: (2024)

Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data
por: Ziatdinov, Maxim
Publicado: (2024)

Phase transitions reveal hierarchical structure in deep neural networks
por: Ersoy, Ibrahim Talha, et al.
Publicado: (2025)

Topological Learning in Multi-Class Data Sets
por: Griffin, Christopher, et al.
Publicado: (2023)

Data augmentation using diffusion models to enhance inverse Ising inference
por: Lim, Yechan, et al.
Publicado: (2025)

Dreaming Learning
por: Londei, Alessandro, et al.
Publicado: (2024)

Simultaneous Dimensionality Reduction: A Data Efficient Approach for Multimodal Representations Learning
por: Abdelaleem, Eslam, et al.
Publicado: (2023)

Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification
por: Sharma, Himanshu, et al.
Publicado: (2024)

Renormalized Graph Representations for Node Classification
por: Caso, Francesco, et al.
Publicado: (2023)

Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides
por: Karumuri, Sharmila, et al.
Publicado: (2023)

Physics-informed neural operators for the in situ characterization of locally reacting sound absorbers
por: Schmid, Jonas M., et al.
Publicado: (2026)

Exploring how deep learning decodes anomalous diffusion via Grad-CAM
por: Bae, Jaeyong, et al.
Publicado: (2024)

Variational Garrote for Statistical Physics-based Sparse and Robust Variable Selection
por: Soh, Hyungjoon, et al.
Publicado: (2025)