May 27ANN Benchmarks with Etienne Dilocker — Weaviate Podcast #16Approximate Nearest Neighbor (ANN) search is one of the core drivers of industrial scale Vector Search [1]. This article and podcast describes techniques to understand exactly how well ANN algorithms solve this problem when benchmarked on different: Dataset Sizes (e.g. 1 million versus…Artificial Intelligence7 min read
Jul 15, 2021Member-onlyDeep Learning with Code DataCode languages, such as Python or Java, have become a core application area of Deep Learning. OpenAI and GitHub have recently unveiled “Copilot” and the corresponding paper describing the technology and underlying “Codex” models. Copilot is powered by taking the GPT-3 language modeling show on the road to datasets made…Artificial Intelligence6 min read
Apr 9, 2021AI Weekly Update Preview — April 12th, 2021This 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…Deep Learning10 min read
Dec 28, 2020AI Weekly Update — December 28th, 2020Computer 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…Artificial Intelligence10 min read
Nov 27, 2020Tokenizing CORD-19 with NLTKScientific 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…Deep Learning5 min read
Published in Towards Data Science·Oct 27, 2020Member-onlySelf-Training for Natural Language UnderstandingThis 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…Artificial Intelligence6 min read
Published in Towards Data Science·Jan 17, 2020Member-onlyThe Evolution of AlphaGo to MuZeroDeepMind’s MuZero algorithm reaches superhuman ability in 57 different Atari games. This article will explain the context leading up to it! — 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…Artificial Intelligence8 min read
Published in Towards Data Science·Dec 17, 2019Member-onlyStyleGAN2This article explores changes made in StyleGAN2 such as weight demodulation, path length regularization and removing progressive growing! — 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. …Deep Learning8 min read
Published in Towards Data Science·Nov 7, 2019600X t-SNE speedup with RAPIDSGPU 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…Machine Learning4 min read
Published in Towards Data Science·Oct 25, 2019Feature Engineering with RAPIDS: NFL Data BowlBefore 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…Data Science4 min read
