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MACHINE LEARNING
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Moving AI from Prototyping to Production at Enterprise Scale
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Measuring AI Research Intuition: Tracking Prediction Accuracy
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In AI research there is tremendous value in intuitions on what makes things work. In fact, this skill is what makes “yolo runs” successful, and can accelerate your team tremendously. However, there’s no track record on how good someone’s intuition is. A fun way to do this is
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Advanced Vector Retrieval with Graph-Based Metadata Using LangChain Neo4j
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Optimizing vector retrieval with advanced graph-based metadata techniques using LangChain and Neo4j Text embeddings and vector similarity search help us find documents by understanding their meanings and how similar they are to each other. However, text embeddings aren’t
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SIMD and Parallelization Optimization Challenges
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yea that and other SIMD / parallelization unoptimizations
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Volume 239: Foundation Models Failure Modes Proceedings Published on PMLR
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Volume 239 Proceedings on "I Can't Believe It's Not Better: Failure Modes in the Age of Foundation Models" https://
proceedings.mlr.press/v239/ Is now available on PMLR. -
New Book Release: Mastering Marketing Data Science for Modern Marketers
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New book release by @IainLJBrown — now available at amzn.to/3UfluKn "Mastering Marketing #DataScience: A Comprehensive Guide for Today's Marketers" 🔵🔵🔵🔵🔵 #Martech #AI #MachineLearning #CMO #Analytics #DataScientist 🌟🌟🌟🌟🌟 See reviews: linkedin.com/pulse/elevate-y…
→ View original post on X — @iainljbrown, 2024-04-25 15:38 UTC
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PyTorch API design constraints for computational efficiency
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pytorch has a history of constraining API to be computationally efficient (in eager), in a "dont let user shoot themselves in the foot" kinda way. this is one of those constraints.
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Fine-tuning Embedding Models: Why BERT Outperforms Larger T5 Encoders
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question: i’m finetuning embedding models for a retrieval. i found large T5 encoders from GTR and SentenceT5 families significantly underperform BERT base how is this possible? feels it must be hyperparameters? unless i found the one NLP task ever where scaling doesn’t help
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In-Context Learning Sensitivity to Shot Order in LLMs
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We also do observe some sensitivity to the order in which the shots are presented in the context window (see Figure 13).
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ICL Robustness: Why In-Context Learning Lacks Monotonic Improvement
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That's a good question! Well, at a minimum I'd say that what we're observing right now is that ICL is not as robust as we'd probably want it to be, as we don't always get the strictly monotonic improvement in performance that you'd expect from a normal learning algorithm.