In the ever-growing field of machine learning, one of the most significant challenges is making complex models interpretable and accessible. Enter AutoXplainAI, an innovative framework developed by ...

Most approaches to machine learning-based inverse design require large amounts of simulation or experimental data, which are costly and time ... metamaterials and future multi-functional materials, ...

Researchers have developed a new machine learning algorithm that excels at interpreting optical spectra, potentially enabling faster and more precise medical diagnoses and sample analysis.

Researchers from Rice University (TX, USA) have developed a new machine learning algorithm that interprets optical spectra of ...

In a recent advance, a multi-disciplinary team of researchers developed a machine learning framework that adapts to changes in the geometry of the physical settings of PDEs. Called DIMON, the new ...

Researchers made a technique that improves the trustworthiness of machine-learning models, which could help improve the accuracy and reliability of AI predictions for high-stakes settings such health ...

Mixture-of-Experts (MoE) models are revolutionizing the way we scale AI. By activating only a subset of a model’s components ...

But that optimism was tempered by a more sobering data point: the U.S. Bureau of Economic Analysis (BEA) released its advance estimate for first-quarter 2025 GDP, and the headline number came in ...

Data science platform Kaggle is hosting a Wikipedia dataset that’s specifically optimized for machine learning applications. Data science platform Kaggle is hosting a Wikipedia dataset ...