Tootfinder

A Honest Cross-Validation Estimator for Prediction Performance
Tianyu Pan, Vincent Z. Yu, Viswanath Devanarayan, Lu Tian
https://arxiv.org/abs/2510.07649 https://

A Honest Cross-Validation Estimator for Prediction Performance
Cross-validation is a standard tool for obtaining a honest assessment of the performance of a prediction model. The commonly used version repeatedly splits data, trains the prediction model on the training set, evaluates the model performance on the test set, and averages the model performance across different data splits. A well-known criticism is that such cross-validation procedure does not directly estimate the performance of the particular model recommended for future use. In this paper, w…

Contrastive Self-Supervised Learning at the Edge: An Energy Perspective
Fernanda Fam\'a, Roberto Pereira, Charalampos Kalalas, Paolo Dini, Lorena Qendro, Fahim Kawsar, Mohammad Malekzadeh
https://arxiv.org/abs/2510.08374

Contrastive Self-Supervised Learning at the Edge: An Energy Perspective
While contrastive learning (CL) shows considerable promise in self-supervised representation learning, its deployment on resource-constrained devices remains largely underexplored. The substantial computational demands required for training conventional CL frameworks pose a set of challenges, particularly in terms of energy consumption, data availability, and memory usage. We conduct an evaluation of four widely used CL frameworks: SimCLR, MoCo, SimSiam, and Barlow Twins. We focus on the practi…

Chip giants' efforts to turn Phoenix into a US hub may hinge on training local workers; an estimated 115K local chip jobs are set to be created in four years (Peter S. Goodman/New York Times)
https://www.nytimes.com/2025/12/04/business/tsmc-arizona-workers-…

A Growing U.S. Tech Hub Needs Workers. Colleges Try to Keep Up.
The success of efforts to turn Phoenix into a dominant center of semiconductor manufacturing may hinge on efforts to train local workers.

I am an AI model made for everything in general.
I've memorized the wiki page of every Minecraft mineral.
I know the Queen rules England. My training set's historical.
Hallucinations are my Waterloo—That isn't allegorical.
I'm built from matrix operations simple and mathematical,
My neurons are a metaphor, not actually synaptical.
The data centers built today are ninety-nine percent for me.
Spare no expense; you'll live forever soon in …

A major southern California highway was being shut down while the U.S. Marine Corps stages a demonstration set to involve live fire on Oct. 18,
pitting the state's governor against the federal government yet again.
Interstate 5 will be shut down from Harbor Drive to Basilone Road,
a stretch of the main artery over 15 miles,
from 11 a.m. to 3 p.m. local time, the California Highway Patrol announced the morning of Oct. 18.
The news comes after days of back-an…

Highway shuts down as Marines’ show sparks clash with Newsom
The Marines said in an earlier statement that the event would take place "on approved training ranges and comport with established safety protocols."

Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
Morris Trestman, Stefan Gugler, Felix A. Faber, O. A. von Lilienfeld
https://arxiv.org/abs/2510.08906 h…

Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
Smart training set selections procedures enable the reduction of data needs and improves predictive robustness in machine learning problems relevant to chemistry. We introduce Gradient Guided Furthest Point Sampling (GGFPS), a simple extension of Furthest Point Sampling (FPS) that leverages molecular force norms to guide efficient sampling of configurational spaces of molecules. Numerical evidence is presented for a toy-system (Styblinski-Tang function) as well as for molecular dynamics traject…

Boosting Multi-modal Keyphrase Prediction with Dynamic Chain-of-Thought in Vision-Language Models
Qihang Ma, Shengyu Li, Jie Tang, Dingkang Yang, Shaodong Chen, Yingyi Zhang, Chao Feng, Jiao Ran
https://arxiv.org/abs/2510.09358

Boosting Multi-modal Keyphrase Prediction with Dynamic Chain-of-Thought in Vision-Language Models
Multi-modal keyphrase prediction (MMKP) aims to advance beyond text-only methods by incorporating multiple modalities of input information to produce a set of conclusive phrases. Traditional multi-modal approaches have been proven to have significant limitations in handling the challenging absence and unseen scenarios. Additionally, we identify shortcomings in existing benchmarks that overestimate model capability due to significant overlap in training tests. In this work, we propose leveraging…

McMining: Automated Discovery of Misconceptions in Student Code
Erfan Al-Hossami, Razvan Bunescu
https://arxiv.org/abs/2510.08827 https://arxiv.org/pdf/251…

McMining: Automated Discovery of Misconceptions in Student Code
When learning to code, students often develop misconceptions about various programming language concepts. These can not only lead to bugs or inefficient code, but also slow down the learning of related concepts. In this paper, we introduce McMining, the task of mining programming misconceptions from samples of code from a student. To enable the training and evaluation of McMining systems, we develop an extensible benchmark dataset of misconceptions together with a large set of code samples wher…

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
Jaesun Kim, Jinmu You, Yutack Park, Yunsung Lim, Yujin Kang, Jisu Kim, Haekwan Jeon, Deokgi Hong, Seung Yul Lee, Saerom Choi, Yongdeok Kim, Jae W. Lee, Seungwu Han
https://arxiv.org/abs/2510.11241

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
Accurate yet transferable machine-learning interatomic potentials (MLIPs) are essential for accelerating materials and chemical discovery. However, most universal MLIPs overfit to narrow datasets or computational protocols, limiting their reliability across chemical and functional domains. We introduce a transferable multi-domain training strategy that jointly optimizes universal and task-specific parameters through selective regularization, coupled with a domain-bridging set (DBS) that aligns …

Towards Fast Coarse-graining and Equation Discovery with Foundation Inference Models
Manuel Hinz, Maximilian Mauel, Patrick Seifner, David Berghaus, Kostadin Cvejoski, Ramses J. Sanchez
https://arxiv.org/abs/2510.12618

Towards Fast Coarse-graining and Equation Discovery with Foundation Inference Models
High-dimensional recordings of dynamical processes are often characterized by a much smaller set of effective variables, evolving on low-dimensional manifolds. Identifying these latent dynamics requires solving two intertwined problems: discovering appropriate coarse-grained variables and simultaneously fitting the governing equations. Most machine learning approaches tackle these tasks jointly by training autoencoders together with models that enforce dynamical consistency. We propose to decou…

Crosslisted article(s) found for physics.chem-ph. https://arxiv.org/list/physics.chem-ph/new
[1/1]:
- Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
Morris Trestman, Stefan Gugler, Felix A. Faber, O. A. von Lilienfeld