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A new framework automates quantum circuit design using graph-based Bayesian optimization and a graph neural network surrogate, achieving competitive classification accuracy on cybersecurity data with reduced circuit comp…
Researchers applied hybrid quantum-classical neural networks to sentiment analysis of COVID-19 tweets, finding that the models matched classical accuracy and showed distinct learning dynamics. In transfer learning tests …
Jim Dowling's O'Reilly book 'Building Machine Learning Systems with a Feature Store' offers a practical guide for deploying machine learning models into production, based on a course he taught at KTH in Stockholm. The bo…
Researchers have developed a model-agnostic framework that integrates privacy-enhancing coded computing with defense mechanisms to protect distributed machine learning systems from privacy breaches and malicious interfer…
Sktime, a Python library for time-series machine learning, provides a scikit-learn-style API for forecasting, classification, and regression. In a tutorial, developers can build temperature forecasting models for HVAC se…
A guide to machine learning fundamentals explains the training loop, supervised learning paradigms, and the bias-variance tradeoff, emphasizing that models are functions optimized to minimize error through proper data sp…
A new beginner's guide to machine learning uses animated visuals and project-based modules to teach fundamentals like linear regression and gradient descent, hosted on the @school_whool YouTube channel. The guide aims to…
Machine learning has fundamentally reoriented scientific practice from inference toward prediction, according to a new analysis of 4.9 million publications and 255 ML techniques by Malena Méndez Isla et al. The study ide…
The VERaiPHY initiative provides a structured framework to evaluate machine learning's trustworthiness in physics, as ML systems become more autonomous in making scientific claims. Physicists are shifting from experiment…
Support Vector Attention (SV-Attention), a new approach to memory in machine learning, achieves certified token deletion with a rare-item recall of 0.86 compared to 0.32 for other methods, according to researchers. The t…
Minghao Li, a fifth-year PhD student in Computer Science at Harvard SEAS, presented a series of works at the intersection of networking and machine learning systems, arguing that distributed ML systems should be designed…
Artificial intelligence startup HeyDonto AI Technology has launched DFT Labs, a research subsidiary focused on developing a physics-based framework for machine learning. The launch follows the publication of the peer-rev…
A machine learning analysis of 38,018 UK Biobank participants has revealed that childhood trauma amplifies genetic risks for depression in ways conventional statistical methods have missed. Yale University researchers us…
Researchers have systematically excluded women, particularly pregnant women, from clinical trials since post-thalidomide FDA guidance, leaving 79% of pregnant women taking at least one medication without adequate safety …
Harvard University and MIT Press released a free, open-source two-volume textbook on machine learning systems, covering AI engineering from single-machine to fleet-scale infrastructure. The curriculum includes interactiv…
A researcher argues that success in machine learning research depends more on discipline and consistent effort than innate talent, comparing the process to meditation. The author advises beginners to focus on fundamental…
A new book, 'Build a Reasoning Model (From Scratch)' (ISBN-13 9781633434677), teaches readers how to add reasoning capabilities to a pre-trained large language model through hands-on code examples, covering inference-tim…
A population-level machine learning analysis of Medical Expenditure Panel Survey data found that healthcare financial vulnerability after COVID-19 remained strongly associated with poverty status, insurance coverage, and…
A machine learning approach to automatic thematic indexing achieves F1 scores up to 0.67 on Voltaire's Complete Works, with the best-performing model from the Mistral family in a 4-bit quantised configuration, according …
Researchers introduced Physics-Audited Agentic Scientific Machine Learning (PA-SciML), a verification-first workflow that ensures surrogate models discovered by LLM agents satisfy physics constraints such as boundary con…