Search Results for author: Philippe Cudré-Mauroux

Found 11 papers, 9 papers with code

Spider4SPARQL: A Complex Benchmark for Evaluating Knowledge Graph Question Answering Systems

1 code implementation28 Sep 2023 Catherine Kosten, Philippe Cudré-Mauroux, Kurt Stockinger

With the recent spike in the number and availability of Large Language Models (LLMs), it has become increasingly important to provide large and realistic benchmarks for evaluating Knowledge Graph Question Answering (KGQA) systems.

Graph Question Answering Knowledge Graphs +3

CORAD: Correlation-Aware Compression of Massive Time Series using Sparse Dictionary Coding

1 code implementation Big Data 2019 Abdelouahab Khelifati, Mourad Khayati, Philippe Cudré-Mauroux

In this work, we demonstrate how one can leverage the correlation across several related time series streams to both drastically improve the compression efficiency and reduce the accuracy loss. We present a novel compression algorithm for time series streams called CORAD (CORelation-Aware compression of time series streams based on sparse Dictionary coding).

Autonomous Vehicles Data Compression +5

A Human-AI Loop Approach for Joint Keyword Discovery and Expectation Estimation in Micropost Event Detection

no code implementations2 Dec 2019 Akansha Bhardwaj, Jie Yang, Philippe Cudré-Mauroux

Such approaches are, however, limited as they fail to reliably estimate the informativeness of a keyword and its expectation for model training.

Event Detection Informativeness

DAOC: Stable Clustering of Large Networks

1 code implementation19 Sep 2019 Artem Lutov, Mourad Khayati, Philippe Cudré-Mauroux

Clustering is a crucial component of many data mining systems involving the analysis and exploration of various data.

Clustering

SECTOR: A Neural Model for Coherent Topic Segmentation and Classification

3 code implementations TACL 2019 Sebastian Arnold, Rudolf Schneider, Philippe Cudré-Mauroux, Felix A. Gers, Alexander Löser

From our extensive evaluation of 20 architectures, we report a highest score of 71. 6% F1 for the segmentation and classification of 30 topics from the English city domain, scored by our SECTOR LSTM model with bloom filter embeddings and bidirectional segmentation.

Classification General Classification +2

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