This article presents a few salient quotes from each of the papers that will be covered on the next AI Weekly Update on Henry AI Labs!

Major Themes of the Latest Papers

• Self-Supervised Learning
• Vision-Language Learning
• Generative Modeling (mostly GANs)
• Meta-Learning
• NLP
• Generalization
• Model-based RL
• Code Examples (GPT-Neo, RAPIDS+Determined, PT Lightning+DeepSpeed)
• Meta (Ruder Newsletter, Commentary on…

Computer Vision, taken over by Transformers

Dear Readers,

Thank you for checking out the AI Weekly Update Newsletter from Henry AI Labs! This newsletter tours updates in Deep Learning and Artificial Intelligence, providing quotes and images that tell each story.

I am working on publishing my first experimental paper in Contrastive Learning. More than anything else, this has…

Scientific overload is one of the toughest challenges facing scientists today. As Machine Learning researchers, we constantly complain about the fast pace of arxiv uploads and praise organization tools like Arxiv Sanity Preserver. The scientific response to COVID-19 is another example of information overload. The CORD-19 dataset documents over 100K…

This article will explain an exciting development in Natural Language Processing. The paper presents a Semi-Supervised Learning algorithm that significantly improves RoBERTa’s performance with Self-Training. If you prefer a video explanation of the paper, please check this out!

Transfer Learning has been extremely successful in Deep Learning. This describes…

DeepMind recently released their MuZero algorithm, headlined by superhuman ability in 57 different Atari games.

Reinforcement Learning agents that can play Atari games are interesting because, in addition to a visually complex state space, agents playing Atari games don’t have a perfect simulator they can use for planning as in…

The first version of the StyleGAN architecture yielded incredibly impressive results on the facial image dataset known as Flicker-Faces-HQ (FFHQ). The most impressive characteristic of these results, compared to early iterations of GANs such as Conditional GANs or DCGANs, is the high resolution (1024²) of the generated images. …

GPU accelerations are commonly associated with Deep Learning. GPUs power Convolutional Neural Networks for Computer Vision and Transformers for Natural Language Processing. They do this through parallel computation, making them much faster for certain tasks compared to CPUs.

RAPIDS is expanding the utilization of GPUs by bringing traditional Machine Learning…

Before starting this article, I want to ease your skepticism of switching from pandas to RAPIDS cudf, RAPIDS cudf uses the same API as pandas!

RAPIDS is moving traditional Data Science workflows on tabular datasets to GPUs. Recently, George Sief posted an article on Towards Data Science showing that the…