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Enhancing Knowledge Discovery: Implementing Retrieval Augmented Generation with Ontotext Technologies

Ontotext

RAG and Ontotext offerings: a perfect synergy RAG is an approach for enhancing an existing large language model (LLM) with external information provided as part of the input prompt, or grounding context. So we have built a dataset using schema.org to model and structure this content into a knowledge graph.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade. See Ribeiro et al.

Modeling 139
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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. We describe experiment designs which have proven effective for us and discuss the subtleties of trying to generalize the results via modeling.

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On the Hunt for Patterns: from Hippocrates to Supercomputers

Ontotext

These are the so-called supercomputers, led by a smart legion of researchers and practitioners in the fields of data-driven knowledge discovery. The capacity and performance of supercomputers is measured with the so-called FLOPS (floating point operations per second). What are supercomputers and why do we need them?

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Performing Non-Compartmental Analysis with Julia and Pumas AI

Domino Data Lab

NCA doesn’t require the assumption of a specific compartmental model for either drug or metabolite; it is instead assumption-free and therefore easily automated [1]. PharmaceUtical Modeling And Simulation (or PUMAS) is a suite of tools to perform quantitative analytics for pharmaceutical drug development [2]. Mean residence time.

Metrics 59
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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation. Ramp-up solution: measure epoch and condition on its effect If one wants to do full traffic ramp-up and use data from all epochs, they must use an adjusted estimator to get an unbiased estimate of the average reward in each arm.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. This renders measures like classification accuracy meaningless. The use of multiple measurements in taxonomic problems. A weighted nearest neighbor algorithm for learning with symbolic features.