This workshop will consider several applications based on machine learning classification and the training of artificial neural networks and deep learning.

Like all AI models based on the Transformer architecture, the large language models (LLMs) that underpin today’s coding ...

Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting ...

PPA constraints need to be paired with real workloads, but they also need to be flexible to account for future changes.

If you use consumer AI systems, you have likely experienced something like AI "brain fog": You are well into a conversation ...

To generate unconditional samples, please run the UnconditionalDiffusionTraining_and_Generation/scripts/inference.py script python UnconditionalDiffusionTraining_and ...

As for the AI bubble, it is coming up for conversation because it is now having a material effect on the economy at large. Some accounts estimate that AI is driving 90% of US GDP growth, while others ...

Abstract: Polar codes with large kernels offer improved error-correction performance but suffer from high decoding complexity due to costly marginalization operations. In this work, we propose neural ...

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 ...

We introduce the NiNo model predicting future (nowcasting) parameters by learning neuron interaction in vision and language tasks. We feed c (c=5 by default) past parameter states as input to NiNo and ...