Andrew Fairless – Page 38 – Andrew Fairless, Ph.D.

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: Should Warehouse Be Immutable?

By Andrew Fairless on June 14, 2022May 23, 2022

https://www.kdnuggets.com/2022/05/data-warehouse-immutable.html Should The Data Warehouse Be Immutable?By Barr Moses, CEO and Co-founder of Monte CarloMay 17, 2022By Barr Moses & Chad Sanderson “As he sees it, data quality and usabilityContinue readingWhat I Read: Should Warehouse Be Immutable?

What I Read: Modern Stack for ML Infrastructure

By Andrew Fairless on June 13, 2022May 23, 2022

https://youtu.be/gZnhSHvhuFQThe Modern Stack for ML Infrastructure | OuterboundsMay 2, 2022Data Council “Metaflow was originally developed at Netflix to provide a user-friendly platform for a wide range of ML use cases…Continue readingWhat I Read: Modern Stack for ML Infrastructure

What I Read: Bandits for Recommender Systems

By Andrew Fairless on June 8, 2022May 23, 2022

https://eugeneyan.com/writing/bandits/ Bandits for Recommender Systemseugeneyan “Recommender systems work well when we have a lot of data on user-item preferences…. Conversely, with very little data, we have low certainty…. Bandits addressContinue readingWhat I Read: Bandits for Recommender Systems

What I Read: ‘Machine Scientists’ Distill the Laws of Physics From Raw Data

By Andrew Fairless on June 7, 2022May 23, 2022

https://www.quantamagazine.org/machine-scientists-distill-the-laws-of-physics-from-raw-data-20220510/ Powerful ‘Machine Scientists’ Distill the Laws of Physics From Raw DataCharlie WoodStaff WriterMay 10, 2022 “Researchers say we’re on the cusp of “GoPro physics,” where a camera can pointContinue readingWhat I Read: ‘Machine Scientists’ Distill the Laws of Physics From Raw Data

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: Learning, not Enough Data Part 3

By Andrew Fairless on June 1, 2022May 23, 2022

https://lilianweng.github.io/posts/2022-04-15-data-gen/ Learning with not Enough Data Part 3: Data GenerationApril 15, 2022Lilian Weng “Here comes the Part 3 on learning with not enough data (Previous: Part 1 and Part 2).Continue readingWhat I Read: Learning, not Enough Data Part 3

What I Read: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval

By Andrew Fairless on May 31, 2022April 25, 2022

https://hazyresearch.stanford.edu/blog/2022-04-19-contrastive-3 TABi: Type-Aware Bi-Encoders for Open-Domain Entity RetrievalApr 19, 2022Megan Leszczynski, Dan Fu, Mayee Chen, and Chris Ré. “We introduce a method to improve retrieval of rare entities by incorporatingContinue readingWhat I Read: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval

What I Read: Supervised Contrastive Learning

By Andrew Fairless on May 30, 2022April 25, 2022

https://hazyresearch.stanford.edu/blog/2022-04-19-contrastive-2 Improving Transfer and Robustness in Supervised Contrastive LearningApr 19, 2022 Mayee Chen, Dan Fu, Avanika Narayan, Michael Zhang, Zhao Song, Kayvon Fatahalian , and Chris Ré. “A major problemContinue readingWhat I Read: Supervised Contrastive Learning

What I Read: Real World Recommendation System

By Andrew Fairless on May 25, 2022April 25, 2022

https://blog.fennel.ai/p/real-world-recommendation-system?s=r Real World Recommendation System – Part 1Nikhil GargApr 10 “Training a collaborative filtering based recommendation system on a toy dataset is a sophomore year project in colleges these days.Continue readingWhat I Read: Real World Recommendation System

Author: Andrew Fairless