859 citations
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24 papers · 1 filter
AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations
Ikhtiyor Nematov, Dimitris Sacharidis, Tomer Sagi +1
For many use-cases, it is often important to explain the prediction of a black-box model by identifying the most influential training data samples. Existing approaches lack customi…
Domain Adaptation for Time series Transformers using One-step fine-tuning
Subina Khanal, Seshu Tirupathi, Giulio Zizzo +2
The recent breakthrough of Transformers in deep learning has drawn significant attention of the time series community due to their ability to capture long-range dependencies. Howev…
View-based Explanations for Graph Neural Networks
Tingyang Chen, Dazhuo Qiu, Yinghui Wu +3
Generating explanations for graph neural networks (GNNs) has been studied to understand their behavior in analytical tasks such as graph classification. Existing approaches aim to…
Meta-Path Learning for Multi-relational Graph Neural Networks
Francesco Ferrini, Antonio Longa, Andrea Passerini +1
Existing multi-relational graph neural networks use one of two strategies for identifying informative relations: either they reduce this problem to low-level weight learning, or th…
Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study
Joakim Edin, Alexander Junge, Jakob D. Havtorn +4
Medical coding is the task of assigning medical codes to clinical free-text documentation. Healthcare professionals manually assign such codes to track patient diagnoses and treatm…
Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation
Qiongxiu Li, Jaron Skovsted Gundersen, Katrine Tjell +2
Privacy has become a major concern in machine learning. In fact, the federated learning is motivated by the privacy concern as it does not allow to transmit the private data but on…