Copertina

Group Averaging for Physics Applications: Accuracy Improvements at Zero Training Cost

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Foit, Valentino F., Hogg, David W., Villar, Soledad
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Is machine learning good or bad for the natural sciences?
di: Hogg, David W., et al.
Pubblicazione: (2024)

Equivariant geometric convolutions for emulation of dynamical systems
di: Gregory, Wilson G., et al.
Pubblicazione: (2023)

Causal Foundation Models: Disentangling Physics from Instrument Properties
di: Audenaert, Jeroen, et al.
Pubblicazione: (2025)

Machine learning and invariant theory
di: Blum-Smith, Ben, et al.
Pubblicazione: (2022)

Embedded Federated Feature Selection with Dynamic Sparse Training: Balancing Accuracy-Cost Tradeoffs
di: Mahanipour, Afsaneh, et al.
Pubblicazione: (2025)

Multi-View Graph Learning with Graph-Tuple
di: Chen, Shiyu, et al.
Pubblicazione: (2025)

SUNLayer: Stable denoising with generative networks
di: Jin, Ruhui, et al.
Pubblicazione: (2018)

A formula for the area of a triangle: Useless, but explicitly in Deep Sets form
di: Hainje, Connor, et al.
Pubblicazione: (2025)

RAZOR: Refining Accuracy by Zeroing Out Redundancies
di: Riccio, Daniel, et al.
Pubblicazione: (2024)

Anchored random clusters and SLE excursions
di: Camia, Federico, et al.
Pubblicazione: (2026)

Trainable Weight Averaging: Accelerating Training and Improving Generalization
di: Li, Tao, et al.
Pubblicazione: (2022)

Learning Rate Scaling across LoRA Ranks and Transfer to Full Finetuning
di: Chen, Nan, et al.
Pubblicazione: (2026)

Primal-Only Actor Critic Algorithm for Robust Constrained Average Cost MDPs
di: Satheesh, Anirudh, et al.
Pubblicazione: (2025)

On the Accuracy and Precision of Moving Averages to Estimate Wi-Fi Link Quality
di: Cena, Gianluca, et al.
Pubblicazione: (2024)

Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics
di: Mercader-Perez, Pablo, et al.
Pubblicazione: (2026)

Performance of NPG in Countable State-Space Average-Cost RL
di: Murthy, Yashaswini, et al.
Pubblicazione: (2024)

Trading-off Accuracy and Communication Cost in Federated Learning
di: Villani, Mattia Jacopo, et al.
Pubblicazione: (2025)

Accuracy Improvement in Differentially Private Logistic Regression: A Pre-training Approach
di: Hoseinpour, Mohammad, et al.
Pubblicazione: (2023)

An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation
di: Alsafadi, Farah, et al.
Pubblicazione: (2024)

Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and Cost
di: Maier, Jannis, et al.
Pubblicazione: (2024)

Forecasting with Deep Learning: Beyond Average of Average of Average Performance
di: Cerqueira, Vitor, et al.
Pubblicazione: (2024)

If you can distinguish, you can express: Galois theory, Stone--Weierstrass, machine learning, and linguistics
di: Blum-Smith, Ben, et al.
Pubblicazione: (2025)

Graph neural networks and non-commuting operators
di: Velasco, Mauricio, et al.
Pubblicazione: (2024)

Achieving Upper Bound Accuracy of Joint Training in Continual Learning
di: Momeni, Saleh, et al.
Pubblicazione: (2025)

Neural Networks with (Low-Precision) Polynomial Approximations: New Insights and Techniques for Accuracy Improvement
di: Zhang, Chi, et al.
Pubblicazione: (2024)

Iterative Assessment and Improvement of DNN Operational Accuracy
di: Guerriero, Antonio, et al.
Pubblicazione: (2023)

How to Achieve Higher Accuracy with Less Training Points?
di: Yang, Jinghan, et al.
Pubblicazione: (2025)

Param$Δ$ for Direct Weight Mixing: Post-Train Large Language Model at Zero Cost
di: Cao, Sheng, et al.
Pubblicazione: (2025)

MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training
di: Yoon, Daegun, et al.
Pubblicazione: (2023)

Provable Target Sample Complexity Improvements as Pre-Trained Models Scale
di: Fukuchi, Kazuto, et al.
Pubblicazione: (2026)

On Transferring Transferability: Towards a Theory for Size Generalization
di: Levin, Eitan, et al.
Pubblicazione: (2025)

Ensembles at Any Cost? Accuracy-Energy Trade-offs in Recommender Systems
di: Nitschke, Jannik, et al.
Pubblicazione: (2026)

The Accuracy Cost of Weakness: A Theoretical Analysis of Fixed-Segment Weak Labeling for Events in Time
di: Martinsson, John, et al.
Pubblicazione: (2025)

Input Adaptive Bayesian Model Averaging
di: Slavutsky, Yuli, et al.
Pubblicazione: (2025)

Long-Term Prediction Accuracy Improvement of Data-Driven Medium-Range Global Weather Forecast
di: Hu, Yifan, et al.
Pubblicazione: (2024)

Tensor learning with orthogonal, Lorentz, and symplectic symmetries
di: Gregory, Wilson G., et al.
Pubblicazione: (2024)

Bandit Simulation for Average Reward Inference
di: Praharaj, Samya, et al.
Pubblicazione: (2026)

Self-Trained Verification for Training- and Test-Time Self-Improvement
di: Wu, Chen Henry, et al.
Pubblicazione: (2026)

EXACT: How to Train Your Accuracy
di: Karpukhin, Ivan, et al.
Pubblicazione: (2022)

3D Optimization for AI Inference Scaling: Balancing Accuracy, Cost, and Latency
di: Jung, Minseok, et al.
Pubblicazione: (2025)