Increasingly, artificial intelligence systems known as deep learning neural networks are used to inform decisions vital to human health and safety, such as...

Will transformers revolutionize computer vision like they did with natural language processing? That’s one of the major research questions investigated by computer vision scientists...

Further reading  GPE, successor features, and related approaches Improving Generalisation for Temporal Difference Learning: The Successor Representation. Peter Dayan. Neural Computation, 1993. Apprenticeship Learning Via Inverse...

Convolutional neural networks (CNNs) achieve state-of-the-art results in tasks such as image classification and object detection. They are used in many diverse applications,...

Over the past few years, machine learning has grown tremendously. But as young as machine learning is as a discipline, the craft of...

Instance segmentation is a challenging computer vision task that requires the prediction of object instances and their per-pixel segmentation mask. This makes it...

In a paper titled “The ‘Criminality From Face’ Illusion” posted this week on Arxiv.org, a trio of researchers surgically debunked recent research that...

Source Last year, I had the privilege of attending the 2019 Neural Information Processing Systems (NeurIPS) conference in Vancouver, Canada....

We’re excited to announce that OpenAI is co-organizing two NeurIPS 2020 competitions with AIcrowd, Carnegie Mellon University, and DeepMind, using Procgen Benchmark and...

This week, as thousands of protestors marched in cities around the U.S. to bring attention to the death of George Floyd, police brutality,...

And Zoox setlles with Elon's Musketeers over purloined IP Roundup OpenAI Microscope: Neural networks, often described as “black boxes”, are complicated; it’s difficult...

(Submitted on 29 Mar 2020) Abstract: We present Catalyst.RL, an open-source PyTorch framework for reproducible and sample efficient reinforcement learning (RL)...