Search Results for author: Tristan Sylvain

Found 11 papers, 3 papers with code

On self-supervised multi-modal representation learning: An application to Alzheimer's disease

no code implementations25 Dec 2020 Alex Fedorov, Lei Wu, Tristan Sylvain, Margaux Luck, Thomas P. DeRamus, Dmitry Bleklov, Sergey M. Plis, Vince D. Calhoun

In this paper, we introduce a way to exhaustively consider multimodal architectures for contrastive self-supervised fusion of fMRI and MRI of AD patients and controls.

General Classification Representation Learning

Cross-Modal Information Maximization for Medical Imaging: CMIM

no code implementations20 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.)

Image Classification

Image-to-image Mapping with Many Domains by Sparse Attribute Transfer

no code implementations23 Jun 2020 Matthew Amodio, Rim Assouel, Victor Schmidt, Tristan Sylvain, Smita Krishnaswamy, Yoshua Bengio

Unsupervised image-to-image translation consists of learning a pair of mappings between two domains without known pairwise correspondences between points.

Translation Unsupervised Image-To-Image Translation

Object-Centric Image Generation from Layouts

no code implementations16 Mar 2020 Tristan Sylvain, Pengchuan Zhang, Yoshua Bengio, R. Devon Hjelm, Shikhar Sharma

In this paper, we start with the idea that a model must be able to understand individual objects and relationships between objects in order to generate complex scenes well.

Layout-to-Image Generation

Locality and compositionality in zero-shot learning

no code implementations ICLR 2020 Tristan Sylvain, Linda Petrini, Devon Hjelm

In this work we study locality and compositionality in the context of learning representations for Zero Shot Learning (ZSL).

Representation Learning Zero-Shot Learning

Joint Learning of Generative Translator and Classifier for Visually Similar Classes

no code implementations15 Dec 2019 ByungIn Yoo, Tristan Sylvain, Yoshua Bengio, Junmo Kim

In this paper, we propose a Generative Translation Classification Network (GTCN) for improving visual classification accuracy in settings where classes are visually similar and data is scarce.

Data Augmentation Domain Adaptation +2

Learning to rank for censored survival data

1 code implementation6 Jun 2018 Margaux Luck, Tristan Sylvain, Joseph Paul Cohen, Heloise Cardinal, Andrea Lodi, Yoshua Bengio

Survival analysis is a type of semi-supervised ranking task where the target output (the survival time) is often right-censored.

Learning-To-Rank Survival Analysis

Deep Learning for Patient-Specific Kidney Graft Survival Analysis

2 code implementations29 May 2017 Margaux Luck, Tristan Sylvain, Héloïse Cardinal, Andrea Lodi, Yoshua Bengio

An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients.

Decision Making Multi-Task Learning +1

Diet Networks: Thin Parameters for Fat Genomics

5 code implementations28 Nov 2016 Adriana Romero, Pierre Luc Carrier, Akram Erraqabi, Tristan Sylvain, Alex Auvolat, Etienne Dejoie, Marc-André Legault, Marie-Pierre Dubé, Julie G. Hussin, Yoshua Bengio

It is based on the idea that we can first learn or provide a distributed representation for each input feature (e. g. for each position in the genome where variations are observed), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units).

Parameter Prediction

Cannot find the paper you are looking for? You can Submit a new open access paper.