Recently, researchers introduced a new representation learning framework that integrates causal inference with graph neural networks—CauSkelNet, which can be used to model the causal relationships and ...

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy ...

Artificial intelligence is now part of our daily lives, with the subsequent pressing need for larger, more complex models.

Understanding the brain's functional architecture is a fundamental challenge in neuroscience. The connections between neurons ...

Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech ...

in this video, we will understand what is Recurrent Neural Network in Deep Learning. Recurrent Neural Network in Deep Learning is a model that is used for Natural Language Processing tasks. It can be ...

Redistricting is an issue for political junkies. Your average American doesn’t obsess over district maps, even when they help determine who’s in the majority. They passed a redistricting plan out of ...

Abstract: In graph neural networks (GNNs), both node features and labels are examples of graph signals. While it is common in graph signal processing to impose signal smoothness constraints in ...

A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the ...