Imagen de Portada

Neural Laplace for learning Stochastic Differential Equations

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Carrel, Adrien
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Artificial Intelligence Probability
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent
por: Schertzer, Adrien, et al.
Publicado: (2024)

Combinatorial Complex Score-based Diffusion Modelling through Stochastic Differential Equations
por: Carrel, Adrien
Publicado: (2024)

Efficient Training of Neural SDEs Using Stochastic Optimal Control
por: Daems, Rembert, et al.
Publicado: (2025)

Beyond Propagation of Chaos: A Stochastic Algorithm for Mean Field Optimization
por: Tankala, Chandan, et al.
Publicado: (2025)

Neural Expectation Operators
por: Qi, Qian
Publicado: (2025)

Neural Structure Learning with Stochastic Differential Equations
por: Wang, Benjie, et al.
Publicado: (2023)

Quantitative CLTs in Deep Neural Networks
por: Favaro, Stefano, et al.
Publicado: (2023)

Neural Network Parameter-optimization of Gaussian pmDAGs
por: Saremi, Mehrzad
Publicado: (2023)

Feature Learning Dynamics in Infinite-Depth Neural Networks
por: Yao, Zihan, et al.
Publicado: (2025)

Explicit Density Approximation for Neural Implicit Samplers Using a Bernstein-Based Convex Divergence
por: de Frutos, José Manuel, et al.
Publicado: (2025)

Approximation to Deep Q-Network by Stochastic Delay Differential Equations
por: Lu, Jianya, et al.
Publicado: (2025)

Neural Brownian Motion
por: Qi, Qian
Publicado: (2025)

Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
por: Oh, YongKyung, et al.
Publicado: (2024)

Robust $Q$-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty
por: Neufeld, Ariel, et al.
Publicado: (2022)

A Complete Decomposition of Stochastic Differential Equations
por: Duffield, Samuel
Publicado: (2026)

Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs
por: Krach, Florian, et al.
Publicado: (2024)

Representative Arm Identification: A fixed confidence approach to identify cluster representatives
por: Gharat, Sarvesh, et al.
Publicado: (2024)

Generalisation of Total Uncertainty in AI: A Theoretical Study
por: Shariatmadar, Keivan
Publicado: (2024)

Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning
por: Kim, Seyeon, et al.
Publicado: (2024)

Deep Conditional Measure Quantization
por: Turinici, Gabriel
Publicado: (2023)

$α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors
por: Schnoor, Ekkehard, et al.
Publicado: (2026)

Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments
por: Kivva, Yaroslav, et al.
Publicado: (2025)

A Unified Theory of $θ$-Expectations
por: Qi, Qian
Publicado: (2025)

Nonparametric Identification of Latent Concepts
por: Zheng, Yujia, et al.
Publicado: (2025)

Greedy Selection under Independent Increments: A Toy Model Analysis
por: Yang, Huitao
Publicado: (2025)

A Mean-Field Theory of $Θ$-Expectations
por: Qi, Qian
Publicado: (2025)

Note on Martingale Theory and Applications
por: Zou, Xiandong
Publicado: (2026)

Advancing Deep Learning through Probability Engineering: A Pragmatic Paradigm for Modern AI
por: Zhang, Jianyi
Publicado: (2025)

Soft-to-Hard Routing in Sparse Mixture-of-Experts Models
por: Rastegar, Reza
Publicado: (2026)

On the Equivalence of Random Network Distillation, Deep Ensembles, and Bayesian Inference
por: Zanger, Moritz A., et al.
Publicado: (2026)

Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations
por: Xu, Jian, et al.
Publicado: (2024)

BULL-ODE: Bullwhip Learning with Neural ODEs and Universal Differential Equations under Stochastic Demand
por: Naik, Nachiket N., et al.
Publicado: (2025)

Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations
por: Huynh, Phuoc-Toan, et al.
Publicado: (2026)

Neural Thermodynamic Laws for Large Language Model Training
por: Liu, Ziming, et al.
Publicado: (2025)

A Neural Scaling Law from Lottery Ticket Ensembling
por: Liu, Ziming, et al.
Publicado: (2023)

DistillKac: Few-Step Image Generation via Damped Wave Equations
por: Han, Weiqiao, et al.
Publicado: (2025)

Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks
por: Nauck, Christian, et al.
Publicado: (2022)

Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations
por: Brogan, Joel, et al.
Publicado: (2024)

Uncertainty Estimation on Graphs with Structure Informed Stochastic Partial Differential Equations
por: Xu, Fred, et al.
Publicado: (2025)

Bayesian Inference with Deep Weakly Nonlinear Networks
por: Hanin, Boris, et al.
Publicado: (2024)