embedding – Page 2 – Andrew Fairless, Ph.D.

What I Read: Estimate Token Importance in LLM Prompts

By Andrew Fairless on November 16, 2023October 5, 2023

https://www.watchful.io/blog/a-surprisingly-effective-way-to-estimate-token-importance-in-llm-prompts A Surprisingly Effective Way to Estimate Token Importance in LLM PromptsShayan Mohanty “Prompting is our primary means of instructing these AIs, yet the process of crafting effective prompts hasContinue readingWhat I Read: Estimate Token Importance in LLM Prompts

What I Read: Optimizing LLM in production

By Andrew Fairless on November 6, 2023October 5, 2023

https://huggingface.co/blog/optimize-llm Optimizing your LLM in productionSeptember 15, 2023Patrick von Platen “…efficient LLM deployment…. pros and cons of adopting lower precision, provide a comprehensive exploration of the latest attention algorithms, andContinue readingWhat I Read: Optimizing LLM in production

What I Read: Multimodal, Embeddings

By Andrew Fairless on October 5, 2023September 4, 2023

https://app.twelvelabs.io/blog/multimodal-embeddings The Multimodal Evolution of Vector EmbeddingsJames LeAugust 9, 2023 “…companies collect and feed hundreds of millions of terabytes of multimodal data into deep learning models. As a result, embeddings…Continue readingWhat I Read: Multimodal, Embeddings

What I Read: Attention Off By One

By Andrew Fairless on September 13, 2023September 4, 2023

https://www.evanmiller.org/attention-is-off-by-one.html Attention Is Off By OneBy Evan MillerJuly 24, 2023 “…the current generation of AI models have an off-by-one error in a crucial place, and it’s making everyone’s Transformer modelsContinue readingWhat I Read: Attention Off By One

What I Read: Few-Shot Learning

By Andrew Fairless on September 12, 2023September 4, 2023

https://saturncloud.io/blog/breaking-the-data-barrier-how-zero-shot-one-shot-and-few-shot-learning-are-transforming-machine-learning/ Breaking the Data Barrier: How Zero-Shot, One-Shot, and Few-Shot Learning are Transforming Machine LearningBy Christophe AttenWednesday, May 17, 2023 “The improvements made over the last almost two decades haveContinue readingWhat I Read: Few-Shot Learning

What I Read: What, Why ChatGPT

By Andrew Fairless on July 12, 2023June 15, 2023

https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/ What Is ChatGPT Doing … and Why Does It Work?February 14, 2023Stephen Wolfram “That ChatGPT can automatically generate something that reads even superficially like human-written text is remarkable, andContinue readingWhat I Read: What, Why ChatGPT

What I Read: Graph Neural Networks

By Andrew Fairless on May 17, 2023March 30, 2023

https://www.assemblyai.com/blog/ai-trends-graph-neural-networks/ AI trends in 2023: Graph Neural NetworksMarco RamponiDeveloper Educator at AssemblyAIMar 21, 2023 “In the last couple of years Graph Neural Networks have quietly become the dark horse behindContinue readingWhat I Read: Graph Neural Networks

What I Read: Relative representations

By Andrew Fairless on April 18, 2023March 30, 2023

https://arxiv.org/abs/2209.15430 Relative representations enable zero-shot latent space communicationLuca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, Emanuele Rodolà “…we propose to adopt pairwise similarities as an alternative data representation,Continue readingWhat I Read: Relative representations

What I Read: recommender system architectures

By Andrew Fairless on March 23, 2023February 6, 2023

https://amatriain.net/blog/RecsysArchitectures Blueprints for recommender system architectures: 10th anniversary editionXavier AmatriainJanuary 29, 2023 “Ten years ago, we published a post in the Netflix tech blog explaining our three-tier architectural approach toContinue readingWhat I Read: recommender system architectures

What I Read: Realtime User Actions in Recommendation

By Andrew Fairless on February 27, 2023January 9, 2023

https://medium.com/pinterest-engineering/how-pinterest-leverages-realtime-user-actions-in-recommendation-to-boost-homefeed-engagement-volume-165ae2e8cde8 How Pinterest Leverages Realtime User Actions in Recommendation to Boost Homefeed Engagement VolumeXue Xia, Software Engineer, Homefeed Ranking; Neng Gu, Software Engineer, Content & User Understanding; Dhruvil Deven Badani,Continue readingWhat I Read: Realtime User Actions in Recommendation

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