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- InsermFR28 papers
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8 papers · 1 filter
OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection
Nicolas Pinon, Robin Trombetta, Carole Lartizien
Unsupervised anomaly detection (UAD) aims to detect anomalies without labeled data, a necessity in many machine learning applications where anomalous samples are rare or not availa…
dCMF: Learning interpretable evolving patterns from temporal multiway data
Christos Chatzis, Carla Schenker, Jérémy E. Cohen +1
Multiway datasets are commonly analyzed using unsupervised matrix and tensor factorization methods to reveal underlying patterns. Frequently, such datasets include timestamps and c…
Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models
Jeremy E. Cohen, Valentin Leplat
Regularized nonnegative low-rank approximations, such as sparse Nonnegative Matrix Factorization or sparse Nonnegative Tucker Decomposition, form an important branch of dimensional…
Joint self-supervised blind denoising and noise estimation
Jean Ollion, Charles Ollion, Elisabeth Gassiat +2
We propose a novel self-supervised image blind denoising approach in which two neural networks jointly predict the clean signal and infer the noise distribution. Assuming that the…
Matrix-wise -constrained Sparse Nonnegative Least Squares
Nicolas Nadisic, Jeremy E Cohen, Arnaud Vandaele +1
Nonnegative least squares problems with multiple right-hand sides (MNNLS) arise in models that rely on additive linear combinations. In particular, they are at the core of most non…
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation
Micah J Sheller, G Anthony Reina, Brandon Edwards +2
Deep learning models for semantic segmentation of images require large amounts of data. In the medical imaging domain, acquiring sufficient data is a significant challenge. Labelin…