Abstract: Recent advancements in deep neural networks heavily rely on large-scale labeled datasets. However, acquiring annotations for large datasets can be challenging due to annotation constraints.

As you begin your hybrid quantum approach, here are the advantages, use cases and limitations to keep in mind.

Information Theory Meets Deep Neural Networks: Theory and Applications. The previous volume can be viewed here: Volume I Deep Neural Networks (DNNs) have become one of the most popular research ...

This guide shows how TPUs crush performance bottlenecks, reduce training time, and offer immense scalability via Google Cloud ...

A new post on Apple’s Machine Learning Research blog shows how much the M5 improved over the M4 when it comes to running a ...

Like other sectors of society, artificial intelligence is fundamentally changing how investors, traders and companies make ...

This valuable study uses mathematical modeling and analysis to address the question of how neural circuits generate distinct low-dimensional, sequential neural dynamics that can change on fast, ...

WiMi innovatively combines the robust feature extraction capabilities of QCNN with the dual-discriminator architecture to construct a hybrid quantum-classical generative adversarial framework. The ...

AI systems still make surprisingly simple mistakes that persist even after extensive training. They also lack the ability to ...

What if a model could forget without losing its mind?” That question now has a technical foothold, thanks to new research from Goodfire.ai that reveals a clean architectural split between memorization ...

The researchers discovered that this separation proves remarkably clean. In a preprint paper released in late October, they ...

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 ...