no code implementations • 12 Oct 2021 • Francis Dutil, Alexandre See, Lisa Di Jorio, Florent Chandelier
In this technical report, we explore the use of homomorphic encryption (HE) in the context of training and predicting with deep learning (DL) models to deliver strict \textit{Privacy by Design} services, and to enforce a zero-trust model of data governance.
no code implementations • 20 Oct 2020 • Tristan Sylvain, Francis Dutil, Tess Berthier, Lisa Di Jorio, Margaux Luck, Devon Hjelm, Yoshua Bengio
In hospitals, data are siloed to specific information systems that make the same information available under different modalities such as the different medical imaging exams the patient undergoes (CT scans, MRI, PET, Ultrasound, etc.)
1 code implementation • 9 Mar 2020 • Qicheng Lao, Mehrzad Mortazavi, Marzieh Tahaei, Francis Dutil, Thomas Fevens, Mohammad Havaei
In this paper, we propose a general framework in continual learning for generative models: Feature-oriented Continual Learning (FoCL).
1 code implementation • 18 Oct 2019 • Mandana Samiei, Tobias Würfl, Tristan Deleu, Martin Weiss, Francis Dutil, Thomas Fevens, Geneviève Boucher, Sebastien Lemieux, Joseph Paul Cohen
Machine learning is bringing a paradigm shift to healthcare by changing the process of disease diagnosis and prognosis in clinics and hospitals.
1 code implementation • ICLR 2021 • Joseph D. Viviano, Becks Simpson, Francis Dutil, Yoshua Bengio, Joseph Paul Cohen
In some prediction tasks, such as for medical images, one may have some images with masks drawn by a human expert, indicating a region of the image containing relevant information to make the prediction.
no code implementations • 25 Sep 2019 • Joseph D Viviano, Becks Simpson, Francis Dutil, Yoshua Bengio, Joseph Paul Cohen
We describe a simple method for taking advantage of such auxiliary labels, by training networks to ignore the distracting features which may be extracted outside of the region of interest, on the training images for which such masks are available.
no code implementations • ICCV 2019 • Qicheng Lao, Mohammad Havaei, Ahmad Pesaranghader, Francis Dutil, Lisa Di Jorio, Thomas Fevens
), and the style, which is usually not well described in the text (e. g., location, quantity, size, etc.).
no code implementations • 16 Apr 2019 • Becks Simpson, Francis Dutil, Yoshua Bengio, Joseph Paul Cohen
With too few samples or too many model parameters, overfitting can inhibit the ability to generalise predictions to new data.
no code implementations • 28 Mar 2019 • Saeid Asgari Taghanaki, Mohammad Havaei, Tess Berthier, Francis Dutil, Lisa Di Jorio, Ghassan Hamarneh, Yoshua Bengio
The scarcity of richly annotated medical images is limiting supervised deep learning based solutions to medical image analysis tasks, such as localizing discriminatory radiomic disease signatures.
1 code implementation • 8 Oct 2018 • Assya Trofimov, Francis Dutil, Claude Perreault, Sebastien Lemieux, Yoshua Bengio, Joseph Paul Cohen
In this work we propose a method to compute continuous embeddings for kmers from raw RNA-seq data, without the need for alignment to a reference genome.
1 code implementation • 18 Jun 2018 • Francis Dutil, Joseph Paul Cohen, Martin Weiss, Georgy Derevyanko, Yoshua Bengio
We find this approach provides an advantage for particular tasks in a low data regime but is very dependent on the quality of the graph used.
1 code implementation • NeurIPS 2017 • Francis Dutil, Caglar Gulcehre, Adam Trischler, Yoshua Bengio
We investigate the integration of a planning mechanism into sequence-to-sequence models using attention.
no code implementations • WS 2017 • Caglar Gulcehre, Francis Dutil, Adam Trischler, Yoshua Bengio
We investigate the integration of a planning mechanism into an encoder-decoder architecture with attention.
1 code implementation • 13 Jun 2017 • Caglar Gulcehre, Francis Dutil, Adam Trischler, Yoshua Bengio
We investigate the integration of a planning mechanism into an encoder-decoder architecture with an explicit alignment for character-level machine translation.
no code implementations • WS 2017 • Sai Rajeswar, Sandeep Subramanian, Francis Dutil, Christopher Pal, Aaron Courville
Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation.