:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Current, Sean, Kumar, Chandan, Gaitonde, Datta, Parthasarathy, Srinivasan
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	https://arxiv.org/abs/2601.22541
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

DiffER: Categorical Diffusion for Chemical Retrosynthesis
di: Current, Sean, et al.
Pubblicazione: (2025)

Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
di: Andre-Sloan, Sebastien, et al.
Pubblicazione: (2026)

Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks
di: He, Yuntian, et al.
Pubblicazione: (2024)

Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs
di: Maneriker, Pranav, et al.
Pubblicazione: (2024)

Adaptive Correction for Ensuring Conservation Laws in Neural Operators
di: Liu, Chaoyu, et al.
Pubblicazione: (2025)

Prediction Is Not Physics: Learning and Evaluating Conserved Quantities in Neural Simulators
di: Bukowski, Andrew, et al.
Pubblicazione: (2026)

Noether's razor: Learning Conserved Quantities
di: van der Ouderaa, Tycho F. A., et al.
Pubblicazione: (2024)

FedCF: Fair Federated Conformal Prediction
di: Srinivasan, Anutam, et al.
Pubblicazione: (2025)

A Generic Framework for Conformal Fairness
di: Vadlamani, Aditya T., et al.
Pubblicazione: (2025)

Graph Navier Stokes Networks
di: Zhao, Zexing, et al.
Pubblicazione: (2026)

Shear-Layer Perturbation Responses from Time-Resolved Schlieren Data
di: Stahl, Spencer L., et al.
Pubblicazione: (2024)

Convolutional Autoencoders, Clustering and POD for Low-dimensional Parametrization of Navier-Stokes Equations
di: Kim, Yongho, et al.
Pubblicazione: (2023)

MedEqualizer: A Framework Investigating Bias in Synthetic Medical Data and Mitigation via Augmentation
di: Salarian, Sama, et al.
Pubblicazione: (2025)

Sample-Efficient Linear Regression with Self-Selection Bias
di: Gaitonde, Jason, et al.
Pubblicazione: (2024)

Long-time Integration of Nonlinear Wave Equations with Neural Operators
di: Lei, Guanhang, et al.
Pubblicazione: (2024)

Deep vs. Shallow: Benchmarking Physics-Informed Neural Architectures on the Biharmonic Equation
di: Srinivasan, Akshay Govind, et al.
Pubblicazione: (2025)

HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs
di: Zhang, Yue, et al.
Pubblicazione: (2024)

Computational Inertia as a Conserved Quantity in Frictionless and Damped Learning Dynamics
di: Karagoz, Atahan
Pubblicazione: (2025)

Auto-Compressing Networks
di: Dorovatas, Vaggelis, et al.
Pubblicazione: (2025)

Better Models and Algorithms for Learning Ising Models from Dynamics
di: Gaitonde, Jason, et al.
Pubblicazione: (2025)

Bypassing the Noisy Parity Barrier: Learning Higher-Order Markov Random Fields from Dynamics
di: Gaitonde, Jason, et al.
Pubblicazione: (2024)

Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers
di: Kohli, Harsh, et al.
Pubblicazione: (2026)

Equilibrium Conserving Neural Operators for Super-Resolution Learning
di: Oommen, Vivek, et al.
Pubblicazione: (2025)

Cross-Layer Subspace Coupling for LLM Compression: A Unifying Framework and Its Empirical Limits
di: Khilar, Snigdha Chandan
Pubblicazione: (2026)

Comparing and Contrasting DLWP Backbones on Navier-Stokes and Atmospheric Dynamics
di: Karlbauer, Matthias, et al.
Pubblicazione: (2024)

COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
di: Wang, Xiyao, et al.
Pubblicazione: (2023)

Banach neural operator for Navier-Stokes equations
di: Zhang, Bo
Pubblicazione: (2025)

Shinbrot Type Criteria for Energy Conservation of the Compressible Navier-Stokes Equations
di: Chen, Ruxuan, et al.
Pubblicazione: (2026)

Capturing Conditional Dependence via Auto-regressive Diffusion Models
di: Huang, Xunpeng, et al.
Pubblicazione: (2025)

AutoCompress: Critical Layer Isolation for Efficient Transformer Compression
di: Thorat, Archit
Pubblicazione: (2026)

Diffeomorphic Latent Neural Operators for Data-Efficient Learning of Solutions to Partial Differential Equations
di: Ahmad, Zan, et al.
Pubblicazione: (2024)

Rolling Ball Optimizer: Learning by ironing out loss landscape wrinkles
di: Belgoumri, Mohammed Djameleddine, et al.
Pubblicazione: (2025)

A Novel Recurrent Neural Network Framework for Prediction and Treatment of Oncogenic Mutation Progression
di: Parthasarathy, Rishab, et al.
Pubblicazione: (2025)

Generalizing PDE Emulation with Equation-Aware Neural Operators
di: Zhu, Qian-Ze, et al.
Pubblicazione: (2025)

Learning $\mathsf{AC}^0$ Under Graphical Models
di: Chandrasekaran, Gautam, et al.
Pubblicazione: (2026)

An Exterior-Embedding Neural Operator Framework for Preserving Conservation Laws
di: Dong, Huanshuo, et al.
Pubblicazione: (2025)

Controlled LLM Decoding via Discrete Auto-regressive Biasing
di: Pynadath, Patrick, et al.
Pubblicazione: (2025)

Efficient Dilated Squeeze and Excitation Neural Operator for Differential Equations
di: Chauhan, Prajwal, et al.
Pubblicazione: (2026)

Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations
di: Shi, Dai, et al.
Pubblicazione: (2026)

Learning Neural Differential Algebraic Equations via Operator Splitting
di: Koch, James, et al.
Pubblicazione: (2024)