embedding – Page 3 – Andrew Fairless, Ph.D.

What I Read: BLOOM Training

By Andrew Fairless on September 12, 2022August 3, 2022

https://huggingface.co/blog/bloom-megatron-deepspeed The Technology Behind BLOOM TrainingStas BekmanPublished July 14, 2022. “…training ever larger language models has become the norm… the hidden knowledge about how to train such models rarely getsContinue readingWhat I Read: BLOOM Training

What I Read: Transformers in computer vision

By Andrew Fairless on September 6, 2022August 3, 2022

https://theaisummer.com/transformers-computer-vision/ Transformers in computer vision: ViT architectures, tips, tricks and improvementsNikolas Adaloglouon2021-10-21 “You are probably already aware of the Vision Transformer (ViT)…. We will address questions like how can youContinue readingWhat I Read: Transformers in computer vision

What I Read: Text Embeddings Visually Explained

By Andrew Fairless on July 26, 2022July 8, 2022

https://txt.cohere.ai/text-embeddings/ Text Embeddings Visually ExplainedMeor AmerJun 28, 2022 “We take a visual approach to gain an intuition behind text embeddings, what use cases they are good for, and how theyContinue readingWhat I Read: Text Embeddings Visually Explained

What I Read: Applying BERT to Speech

By Andrew Fairless on June 27, 2022June 27, 2022

https://thegradient.pub/an-illustrated-tour-of-applying-bert-to-speech-data/ An Illustrated Tour of Applying BERT to Speech DataJonathan Boigne10.May.2022 “Could we replace the text input in BERT with a speech sequence, mask a part of it, and similarlyContinue readingWhat I Read: Applying BERT to Speech

What I Read: Beyond Message Passing, Graph Neural Networks

By Andrew Fairless on June 6, 2022May 23, 2022

https://thegradient.pub/graph-neural-networks-beyond-message-passing-and-weisfeiler-lehman/ Beyond Message Passing: a Physics-Inspired Paradigm for Graph Neural NetworksMichael Bronstein07.May.2022 “…the “node and edge-centric” mindset of current graph deep learning schemes imposes strong limitations… we propose physics-inspired “continuous”Continue readingWhat I Read: Beyond Message Passing, Graph Neural Networks

What I Read: Graph Neural Networks, Differential Geometry, Algebraic Topology

By Andrew Fairless on January 3, 2022December 11, 2021

https://towardsdatascience.com/graph-neural-networks-through-the-lens-of-differential-geometry-and-algebraic-topology-3a7c3c22d5f Graph Neural Networks through the lens of Differential Geometry and Algebraic TopologyMichael Bronstein “Differential geometry and algebraic topology are not encountered very frequently in mainstream machine learning… tools fromContinue readingWhat I Read: Graph Neural Networks, Differential Geometry, Algebraic Topology

What I Read: Dense Vectors

By Andrew Fairless on December 14, 2021November 16, 2021

https://www.pinecone.io/learn/dense-vector-embeddings-nlp/ Dense Vectors: Capturing Meaning with CodeJames BriggsData Scientist “But since the days of word2vec, developments in representing language have advanced at ludicrous speeds. This article will explore why weContinue readingWhat I Read: Dense Vectors

What I Read: demystifying graph deep learning

By Andrew Fairless on August 30, 2021August 17, 2021

https://ericmjl.github.io/essays-on-data-science/machine-learning/graph-nets/ An attempt at demystifying graph deep learningEric Ma “There are a ton of great explainers of what graph neural networks are. However, I find that a lot of themContinue readingWhat I Read: demystifying graph deep learning

What I Read: Zero-Shot Learning

By Andrew Fairless on May 6, 2021April 13, 2021

https://www.kdnuggets.com/2021/04/zero-shot-learning.html Zero-Shot Learning: Can you classify an object without seeing it before?Developing machine learning models that can perform predictive functions on data it has never seen before has become anContinue readingWhat I Read: Zero-Shot Learning

What I Read: Deep Learning Recommendation Models

By Andrew Fairless on April 24, 2021April 10, 2021

https://www.kdnuggets.com/2021/04/deep-learning-recommendation-models-dlrm-deep-dive.html Deep Learning Recommendation Models (DLRM): A Deep DiveBy Nishant Kumar, Data Science Professional. “This deep dive article presents the architecture and deployment issues experienced with the deep learning recommendationContinue readingWhat I Read: Deep Learning Recommendation Models

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