Researchers from the High Energy Nuclear Physics Laboratory at the RIKEN Pioneering Research Institute (PRI) in Japan and ...

Neural and computational evidence reveals that real-world size is a temporally late, semantically grounded, and hierarchically stable dimension of object representation in both human brains and ...

Wolfram-like attention framing meets spiking networks: event-triggered, energy-thrifty AI that “wakes” to stimuli.

While some AI courses focus purely on concepts, many beginner programs will touch on programming. Python is the go-to ...

Here are 11 free NPTEL data science and analytics courses from leading IITs cover graph theory, Bayesian modelling, Python, R ...

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...

An AI-driven computational toolkit, Gcoupler, integrates ligand design, statistical modeling, and graph neural networks to predict endogenous metabolites that allosterically modulate the GPCR–Gα ...

The 70-meter antenna, designated DSS-14, at the Deep Space Network site in Goldstone, California. Credit: NASA WASHINGTON — One of the largest antennas in NASA’s Deep Space Network was damaged in ...

Broadcast TV continues to draw big audiences. And they are increasingly getting special events, award shows and sports — lots of sports. By John Koblin MTV’s “Video Music Awards” had been a cable-only ...

Massive computing systems are required to train neural networks. The prodigious amount of consumed energy makes the creation of AI applications significant polluters ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...