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

Class Disrupted guest Irina Jurenka on large language models in education: ‘The stakes are so much higher in learning than in other use cases.’ ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

DR Tulu-8B is the first open Deep Research (DR) model trained for long-form DR tasks. DR Tulu-8B matches OpenAI DR on long-form DR benchmarks. agent/: Agent library (dr-agent-lib) with MCP-based tool ...

The rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for ...

Learn about DenseNet, one of the most powerful deep learning architectures, in this beginner-friendly tutorial. Understand its structure, advantages, and how it’s used in real-world AI applications.

Abstract: Recently, quantum deep reinforcement learning (Q-DRL) has started to gain attention as a potential approach for tackling complex challenges in wireless communication systems. In particular, ...

So, you've binged a few treasure-hunting shows and now you're wondering if your own old detector in the garage can find you a pirate chest. One of the first questions that may pop up in your head ...

A new machine learning approach that draws inspiration from the way the human brain seems to model and learn about the world has proven capable of mastering a number of simple video games with ...