This AI research graph edition covers the key knowledge areas and important research papers related to Residual Neural Network (ResNet), including Kaiming He's 10-min presentation of "Deep Residual Learning for Image Recognition” paper.
A comprehensive graph mapping of knowledge areas and research paper presentations related to the new paper "Towards Causal Representation Learning" led by Yoshua Bengio and Bernhard Schölkopf.
From AlexNet to GPT-3, we curate a list of 10 papers that mark significant research advancements in machine learning, deep learning, computer vision, NLP, and reinforcement learning over the past 10 years. Author presentation and detailed paper reviews are also included.
Learn the research path of Google AI Language team research scientist Kristina Toutanova. Check out notable publications throughout her career on modeling the structure of natural language using machine learning.
The year 2020 has presented many challenges, but it did not stop new AI research breakthroughs from the global community. Here is a list of 10 best papers and their presentations from this year's top AI conferences across machine learning, computer vision, NLP, robotics, and more.
Robin.ly interviews the CVPR 2019 Best Paper Award winner recipients Shumian Xin and Ioannis Gkioulekas of Carnegie Mellon University. They will share their experience, research, and insights on working on this paper.
Demystify the machine learning models behind new AI applications by Google, Facebook, Amazon, Twitter, and Pinterest.
This blogs features NeurIPS 2019 Outstanding New Directions Paper Award winners, Vaishnavh Nagarajan and J. Zico Kolter. They explain how negative results showing that many existing bounds on the performance of deep learning algorithms don’t do what they claim.
Graph Neural Networks (GNNs) has seen rapid development recently. This blog provides a quick introduction to the methodology and summarizes some of the latest research on GNNs from top AI conferences.
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