#machine-learning

#machine-learning

The research proposes that the creativity of diffusion models stems from a deterministic process linked to how they employ the denoising mechanism to create images.

The copies used to train specific LLMs were justified as fair use. Every factor but the nature of the copyrighted work favors this result. The technology at issue was among the most transformative many of us will see in our lifetimes.

Apple researchers investigated the abilities of Large Reasoning Models (LRMs) on puzzles, revealing a critical collapse threshold that limits their reasoning scalability.

Pre-training on large datasets like ImageNet-21K and fine-tuning for specific tasks enhances model convergence and performance.

To bridge the gap in public time series datasets for pre-training models, we collate multiple task-specific datasets into the Time Series Pile, encompassing diverse domains.

Based on their findings, the scientists are calling for air safety authorities and airlines to be 'more vigilant during takeoff and landing in a warming world.'

Large language models are transforming software development unevenly, with notable advancements in execution phases but limited impact on strategies and fundamental design processes.

"Until the panel decision, this Court had never suggested that Alice step one requires a patent to claim both (1) a new application of an established process and (2) an improvement to the technology used in that process."

“Gemini 2.5 models are thinking models, capable of reasoning through thoughts before responding, resulting in enhanced performance and improved accuracy.”

This study presents a theoretical framework revealing how Transformer models, particularly through associative memories, encapsulate the dynamics of memorization and generalization in language processing.

We're not going to focus on the machine learning itself, but rather the infrastructure that we built to support all these diverse use cases.

AppLovin experienced stock volatility in early 2025, but recovered thanks to strong earnings, with a year-to-date increase of 7% and nearly 4.5x growth since last year.

At Atlantic Labs, we back category-defining technologies - and Omnisent is exactly that. The team combines deep scientific expertise from Cambridge University and Imperial College with impressive execution.

Few-shot learning techniques are transformative in remote sensing, enhancing the capability to classify images from multiple sensors with minimal training data, aiding in real-time analytics.