Nvidia used NATTEN to fine-tune its prediction model, Cosmos Predict, and the company introduced the second version of the ...
IBM released all the Granite 4 Nano models under the open-source Apache 2.0 license, which is highly permissive. The license ...
This blog post is the second in our Neural Super Sampling (NSS) series. The post explores why we introduced NSS and explains its architecture, training, and inference components. In August 2025, we ...
Abstract: Brains evolve within specific sensory and physical environments, yet neuroscience has traditionally focused on ...
A new technical paper titled “Optimizing event-based neural networks on digital neuromorphic architecture: a comprehensive design space exploration” was published by imec, TU Delft and University of ...
A neural interface framework integrating L2 regularization with attention supervision paradigms achieves 96.87% classification accuracy in EEG ...
Deep Learning with Yacine on MSN
Inception Net V1 Explained: Step-by-Step PyTorch Implementation
Learn how the Inception Net V1 architecture works and how to implement it from scratch using PyTorch. Perfect for deep ...
An MIT spinoff co-founded by robotics luminary Daniela Rus aims to build general-purpose AI systems powered by a relatively new type of AI model called a liquid neural network. The spinoff, aptly ...
Article reviewed by Grace Lindsay, PhD from New York University. Scientists design ANNs to function like neurons. 6 They write lines of code in an algorithm such that there are nodes that each contain ...
What are convolutional neural networks in deep learning? Convolutional neural networks are used in computer vision tasks, which employ convolutional layers to extract features from input data.
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