Search Results for author: Laurent Boué

Found 7 papers, 2 papers with code

A case study of Generative AI in MSX Sales Copilot: Improving seller productivity with a real-time question-answering system for content recommendation

no code implementations4 Jan 2024 Manpreet Singh, Ravdeep Pasricha, Nitish Singh, Ravi Prasad Kondapalli, Manoj R, Kiran R, Laurent Boué

In this paper, we design a real-time question-answering system specifically targeted for helping sellers get relevant material/documentation they can share live with their customers or refer to during a call.

Question Answering Recommendation Systems

Searching, fast and slow, through product catalogs

no code implementations1 Jan 2024 Dayananda Ubrangala, Juhi Sharma, Sharath Kumar Rangappa, Kiran R, Ravi Prasad Kondapalli, Laurent Boué

String matching algorithms in the presence of abbreviations, such as in Stock Keeping Unit (SKU) product catalogs, remains a relatively unexplored topic.

Language Modelling

Improving search relevance of Azure Cognitive Search by Bayesian optimization

no code implementations13 Dec 2023 Nitin Agarwal, Ashish Kumar, Kiran R, Manish Gupta, Laurent Boué

Azure Cognitive Search (ACS) has emerged as a major contender in "Search as a Service" cloud products in recent years.

Bayesian Optimization

A Data Source Dependency Analysis Framework for Large Scale Data Science Projects

no code implementations15 Dec 2022 Laurent Boué, Pratap Kunireddy, Pavle Subotić

Dependency hell is a well-known pain point in the development of large software projects and machine learning (ML) code bases are not immune from it.

Real numbers, data science and chaos: How to fit any dataset with a single parameter

2 code implementations28 Apr 2019 Laurent Boué

We show how any dataset of any modality (time-series, images, sound...) can be approximated by a well-behaved (continuous, differentiable...) scalar function with a single real-valued parameter.

BIG-bench Machine Learning Time Series +1

Deep learning for pedestrians: backpropagation in CNNs

1 code implementation29 Nov 2018 Laurent Boué

The goal of this document is to provide a pedagogical introduction to the main concepts underpinning the training of deep neural networks using gradient descent; a process known as backpropagation.

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