Regularization in Deep Learning is very important to overcome overfitting. When your training accuracy is very high, but test accuracy is very low, the model highly overfits the training dataset set ...

A research team has introduced a lightweight artificial intelligence method that accurately identifies wheat growth stages ...

Reinforcement learning frames trading as a sequential decision-making problem, where an agent observes market conditions, ...

Machine learning models are designed to analyze large, heterogeneous datasets and identify complex relationships without ...

By transferring temporal knowledge from complex time-series models to a compact model through knowledge distillation and attention mechanisms, the ...

Researchers have created a deep learning method to analyze social media images taken within protected green spaces to gain insights on human activity distribution as a way to monitor the ecological ...

The first step of conducting document review with generative AI, defining what documents require review, resembles ...

Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...

Apple's researchers continue to focus on multimodal LLMs, with studies exploring their use for image generation, ...

Credit: VentureBeat made with Flux.2 [klein] on Fal.ai The German AI startup Black Forest Labs (BFL), founded by former ...