machine learning – Page 6 – Andrew Fairless, Ph.D.

What I Read: LLM evaluation

By Andrew Fairless on August 7, 2024May 25, 2024

https://huggingface.co/blog/clefourrier/llm-evaluation Let’s talk about LLM evaluationMay 23, 2024Clémentine Fourrier “There are, to my knowledge, at the moment, 3 main ways to do evaluation: automated benchmarking, using humans as judges, andContinue readingWhat I Read: LLM evaluation

What I Read: LLM, DSPy Assertions and Suggestions

By Andrew Fairless on August 6, 2024May 25, 2024

https://learnbybuilding.ai/tutorials/guiding-llm-output-with-dspy-assertions-and-suggestions Guiding LLM Output with DSPy Assertions and SuggestionsBill Chambers “Assertions in DSPy allow you to define strict rules and constraints that the LLM’s output must (or maybe that youContinue readingWhat I Read: LLM, DSPy Assertions and Suggestions

What I Read: AI, Light-Based Chips

By Andrew Fairless on July 31, 2024May 24, 2024

https://www.quantamagazine.org/ai-needs-enormous-computing-power-could-light-based-chips-help-20240520 AI Needs Enormous Computing Power. Could Light-Based Chips Help?Amos Zeeberg5/20/24 10:40 AM “Optical neural networks, which use photons instead of electrons, have advantages over traditional systems. They also faceContinue readingWhat I Read: AI, Light-Based Chips

What I Read: Labeling, Uncertainty Sampling

By Andrew Fairless on July 30, 2024May 24, 2024

https://code.groundlight.ai/python-sdk/blog/active-learning Reducing Data Labeling Costs with Uncertainty SamplingTed SandlerMay 15, 2024Senior Applied Scientist “With active learning, only informative images are prioritized for review, making it possible to label small aContinue readingWhat I Read: Labeling, Uncertainty Sampling

What I Read: Platonic Hypothesis

By Andrew Fairless on July 29, 2024May 24, 2024

https://phillipi.github.io/prh The Platonic Representation HypothesisMinyoung Huh, Brian Cheung, Tongzhou Wang, Phillip IsolaMITPosition Paper in ICML 2024 “Neural networks, trained with different objectives on different data and modalities, are converging toContinue readingWhat I Read: Platonic Hypothesis

What I Read: Game Theory, AI

By Andrew Fairless on July 25, 2024May 24, 2024

https://www.quantamagazine.org/game-theory-can-make-ai-more-correct-and-efficient-20240509 Game Theory Can Make AI More Correct and EfficientSteve NadisMay 9, 2024 “Researchers are drawing on ideas from game theory to improve large language models and make them moreContinue readingWhat I Read: Game Theory, AI

What I Read: StandardScaler

By Andrew Fairless on July 24, 2024May 24, 2024

https://www.youtube.com/watch?v=VZxqqH58F5M The StandardScaler is not Standard:probabl. “There is a scaler in scikit-learn called the “StandardScaler”. The name might imply that it is standard or fairly basic., but once you considerContinue readingWhat I Read: StandardScaler

What I Read: Matryoshka Embedding

By Andrew Fairless on July 17, 2024May 6, 2024

https://huggingface.co/blog/matryoshka Introduction to Matryoshka Embedding ModelsTom AarsenJoshuaOmar SansevieroFebruary 23, 2024 “…Kusupati et al. (2022) were inspired to create embedding models whose embeddings could reasonably be shrunk without suffering too muchContinue readingWhat I Read: Matryoshka Embedding

What I Read: LLMs, Open Source

By Andrew Fairless on July 15, 2024May 6, 2024

https://www.aidancooper.co.uk/how-to-beat-proprietary-llms How to Beat Proprietary LLMs With Smaller Open Source ModelsAidan CooperApr 26, 2024 “…we explore the unique advantages of open source LLMs, and how you can leverage them toContinue readingWhat I Read: LLMs, Open Source

Tag: machine learning