Training deep neural networks like Transformers is challenging. They suffering from vanishing gradients, ineffective weight ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, structureless data. Yet when trained on datasets with structure, they learn the ...

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 ...

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

Decreasing Precision with layer Capacity trains deep neural networks with layer-wise shrinking precision, cutting cost by up to 44% and boosting accuracy by up to 0.68% ...

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, innovatively launches a quantum-enhanced deep convolutional neural network image 3D reconstruction ...

Compared to other regression techniques, a well-tuned neural network regression system can produce the most accurate prediction model, says Dr. James McCaffrey of Microsoft Research in presenting this ...