86 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre Inria d'Université Côte d'AzurFR2 papers
- Institut national de recherche en sciences et technologies du numériqueFR2 papers
- Trinity College DublinIE2 papers
- University of AberdeenGB2 papers
- WIMMICS: Web-Instrumented huMan-Machine Interactions, Communities and SemanticsFR2 papers
- Zhejiang UniversityCN2 papers
- ARGO: Apprentissage, graphes et optimisation distribuéeFR1 paper
- Dublin City UniversityIE1 paper
- EPIONE: E-Patient : Images, données & mOdèles pour la médeciNe numériquEFR1 paper
- Technological University DublinIE1 paper
- University of OxfordGB1 paper
- Zhejiang University of Science and TechnologyCN1 paper
8 papers
Evaluating the Energy Consumption of Machine Learning: Systematic Literature Review and Experiments
Charlotte Rodriguez, Laura Degioanni, Laetitia Kameni +2
Monitoring, understanding, and optimizing the energy consumption of Machine Learning (ML) are various reasons why it is necessary to evaluate the energy usage of ML. However, there…
SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization
Yann Fraboni, Martin Van Waerebeke, Kevin Scaman +3
Machine Unlearning (MU) is an increasingly important topic in machine learning safety, aiming at removing the contribution of a given data point from a training procedure. Federate…
Poisoning Knowledge Graph Embeddings via Relation Inference Patterns
Peru Bhardwaj, John Kelleher, Luca Costabello +1
We study the problem of generating data poisoning attacks against Knowledge Graph Embedding (KGE) models for the task of link prediction in knowledge graphs. To poison KGE models,…
STaDA: Style Transfer as Data Augmentation
Xu Zheng, Tejo Chalasani, Koustav Ghosal +2
The success of training deep Convolutional Neural Networks (CNNs) heavily depends on a significant amount of labelled data. Recent research has found that neural style transfer alg…
Interpretable Credit Application Predictions With Counterfactual Explanations
Rory Mc Grath, Luca Costabello, Chan Le Van +4
We predict credit applications with off-the-shelf, interchangeable black-box classifiers and we explain single predictions with counterfactual explanations. Counterfactual explanat…
Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction
Jinhua Du, Jingguang Han, Andy Way +1
Attention mechanisms are often used in deep neural networks for distantly supervised relation extraction (DS-RE) to distinguish valid from noisy instances. However, traditional 1-D…
Knowledge-based Transfer Learning Explanation
Jiaoyan Chen, Freddy Lecue, Jeff Z. Pan +2
Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation…
Learning from Ontology Streams with Semantic Concept Drift
Freddy Lecue, Jiaoyan Chen, Jeff Pan +1
Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and…