:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Totounferoush, Amin, Kotchourko, Serge, Mahoney, Michael W., Staab, Steffen
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2503.19081
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning
by: Schneider, Tim, et al.
Published: (2023)

The False Promise of Zero-Shot Super-Resolution in Machine-Learned Operators
by: Sakarvadia, Mansi, et al.
Published: (2025)

General surgery vision transformer: A video pre-trained foundation model for general surgery
by: Schmidgall, Samuel, et al.
Published: (2024)

Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction
by: Baier, Alexandra, et al.
Published: (2021)

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data
by: Pasini, Massimiliano Lupo, et al.
Published: (2025)

LMT-Net: Lane Model Transformer Network for Automated HD Mapping from Sparse Vehicle Observations
by: Mink, Michael, et al.
Published: (2024)

Enhancing next token prediction based pre-training for jet foundation models
by: Birk, Joschka, et al.
Published: (2025)

Expanding Expressivity in Transformer Models with MöbiusAttention
by: Halacheva, Anna-Maria, et al.
Published: (2024)

PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs
by: Brivio, Simone, et al.
Published: (2024)

Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs
by: Mouli, S. Chandra, et al.
Published: (2024)

Using Pre-trained LLMs for Multivariate Time Series Forecasting
by: Wolff, Malcolm L., et al.
Published: (2025)

Tabular foundation models for robust calibration of near-infrared chemical sensing data
by: Reiter, Robin, et al.
Published: (2026)

BMFM-DNA: A SNP-aware DNA foundation model to capture variant effects
by: Li, Hongyang, et al.
Published: (2025)

Geometry-aware training of factorized layers in tensor Tucker format
by: Zangrando, Emanuele, et al.
Published: (2023)

Geometric Structural Knowledge Graph Foundation Model
by: Xin, Ling, et al.
Published: (2025)

Nonideality-aware training makes memristive networks more robust to adversarial attacks
by: Joksas, Dovydas, et al.
Published: (2024)

Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning
by: Zhang, Yuanzhao, et al.
Published: (2025)

Found-RL: foundation model-enhanced reinforcement learning for autonomous driving
by: Qu, Yansong, et al.
Published: (2026)

Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models
by: Liao, Zhenyu, et al.
Published: (2025)

Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models
by: Donhauser, Konstantin, et al.
Published: (2024)

NavMapFusion: Diffusion-based Fusion of Navigation Maps for Online Vectorized HD Map Construction
by: Monninger, Thomas, et al.
Published: (2025)

Distributionally robust risk evaluation with an isotonic constraint
by: Gui, Yu, et al.
Published: (2024)

The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression
by: Hassani, Hamed, et al.
Published: (2022)

Fairness Implications of Encoding Protected Categorical Attributes
by: Mougan, Carlos, et al.
Published: (2022)

Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning
by: Chen, Paula, et al.
Published: (2023)

How green is continual learning, really? Analyzing the energy consumption in continual training of vision foundation models
by: Trinci, Tomaso, et al.
Published: (2024)

Reliable edge machine learning hardware for scientific applications
by: Baldi, Tommaso, et al.
Published: (2024)

Universal generalization guarantees for Wasserstein distributionally robust models
by: Le, Tam, et al.
Published: (2024)

Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning
by: Dereziński, Michał, et al.
Published: (2024)

Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
by: Na, Sen, et al.
Published: (2022)

Fast constrained sampling in pre-trained diffusion models
by: Graikos, Alexandros, et al.
Published: (2024)

Extraction of linearized models from pre-trained networks via knowledge distillation
by: Kimura, Fumito, et al.
Published: (2026)

Towards a general-purpose foundation model for fMRI analysis
by: Wang, Cheng, et al.
Published: (2025)

TempBEV: Improving Learned BEV Encoders with Combined Image and BEV Space Temporal Aggregation
by: Monninger, Thomas, et al.
Published: (2024)

eCP: Equivariant Conformal Prediction with pre-trained models
by: Bousias, Nikolaos, et al.
Published: (2026)

RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains
by: Mousavi, Sepehr, et al.
Published: (2025)

Certainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guarantees
by: Zhu, Yuqicheng, et al.
Published: (2025)

An empirical study of task and feature correlations in the reuse of pre-trained models
by: Mohamud, Jama Hussein, et al.
Published: (2025)

Universal pre-training by iterated random computation
by: Bloem, Peter
Published: (2025)

Decision-aware training of spatiotemporal forecasting models to select a top K subset of sites for intervention
by: Heuton, Kyle, et al.
Published: (2025)