Search Results for author: Georgiana Ifrim

Found 13 papers, 8 papers with code

Automated Mobility Context Detection with Inertial Signals

no code implementations16 May 2022 Antonio Bevilacqua, Lisa Alcock, Brian Caulfield, Eran Gazit, Clint Hansen, Neil Ireson, Georgiana Ifrim

We explore two different approaches to this task: (1) using gait descriptors and features extracted from the input inertial signals sampled during walking episodes, together with classic machine learning algorithms, and (2) treating the input inertial signals as time series data and leveraging end-to-end state-of-the-art time series classifiers.

Time Series Time Series Classification

MrSQM: Fast Time Series Classification with Symbolic Representations

1 code implementation2 Sep 2021 Thach Le Nguyen, Georgiana Ifrim

The key idea is to transform numerical time series to symbolic representations in the time or frequency domain, i. e., sequences of symbols, and then extract features from these sequences.

Classification feature selection +2

Mid infrared spectroscopy and milk quality traits: a data analysis competition at the "International Workshop on Spectroscopy and Chemometrics 2021"

1 code implementation5 Jul 2021 Maria Frizzarin, Antonio Bevilacqua, Bhaskar Dhariyal, Katarina Domijan, Federico Ferraccioli, Elena Hayes, Georgiana Ifrim, Agnieszka Konkolewska, Thach Le Nguyen, Uche Mbaka, Giovanna Ranzato, Ashish Singh, Marco Stefanucci, Alessandro Casa

A chemometric data analysis challenge has been arranged during the first edition of the "International Workshop on Spectroscopy and Chemometrics", organized by the Vistamilk SFI Research Centre and held online in April 2021.

Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset

no code implementations15 Mar 2021 Adrianna Janik, Jonathan Dodd, Georgiana Ifrim, Kris Sankaran, Kathleen Curran

In previous studies, the base method is applied to the classification of cardiac disease and provides clinically meaningful explanations for the predictions of a black-box deep learning classifier.

MRI segmentation

Background Knowledge Injection for Interpretable Sequence Classification

no code implementations25 Jun 2020 Severin Gsponer, Luca Costabello, Chan Le Van, Sumit Pai, Christophe Gueret, Georgiana Ifrim, Freddy Lecue

Sequence classification is the supervised learning task of building models that predict class labels of unseen sequences of symbols.

Activity Recognition Classification +1

A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal

1 code implementation ACL 2020 Demian Gholipour Ghalandari, Chris Hokamp, Nghia The Pham, John Glover, Georgiana Ifrim

Multi-document summarization (MDS) aims to compress the content in large document collections into short summaries and has important applications in story clustering for newsfeeds, presentation of search results, and timeline generation.

Document Summarization Multi-Document Summarization

Examining the State-of-the-Art in News Timeline Summarization

1 code implementation ACL 2020 Demian Gholipour Ghalandari, Georgiana Ifrim

Previous work on automatic news timeline summarization (TLS) leaves an unclear picture about how this task can generally be approached and how well it is currently solved.

Timeline Summarization

Story Disambiguation: Tracking Evolving News Stories across News and Social Streams

no code implementations16 Aug 2018 Bichen Shi, Thanh-Binh Le, Neil Hurley, Georgiana Ifrim

This is particularly the case for local news stories that are easily over shadowed by other trending stories, and for complex news stories with ambiguous content in noisy stream environments.

Entity Disambiguation Learning-To-Rank

Interpretable Time Series Classification using All-Subsequence Learning and Symbolic Representations in Time and Frequency Domains

1 code implementation12 Aug 2018 Thach Le Nguyen, Severin Gsponer, Iulia Ilie, Georgiana Ifrim

In this work we analyse the state-of-the-art for time series classification, and propose new algorithms that aim to maintain the classifier accuracy and efficiency, but keep interpretability as a key design constraint.

feature selection General Classification +2

A Machine Learning Approach to Predicting the Smoothed Complexity of Sorting Algorithms

no code implementations23 Mar 2015 Bichen Shi, Michel Schellekens, Georgiana Ifrim

Smoothed analysis is a framework for analyzing the complexity of an algorithm, acting as a bridge between average and worst-case behaviour.

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