Portrait of Tristan Sylvain is unavailable

Tristan Sylvain

Alumni

Publications

Rejecting Hallucinated State Targets during Planning
Mingde “Harry” Zhao
Mingde “Harry” Zhao
Tristan Sylvain
Romain Laroche
Doina Precup
Yoshua Bengio
In planning processes of computational decision-making agents, generative or predictive models are often used as "generators" to propose "ta… (see more)rgets" representing sets of expected or desirable states. Unfortunately, learned models inevitably hallucinate infeasible targets that can cause delusional behaviors and safety concerns. We first investigate the kinds of infeasible targets that generators can hallucinate. Then, we devise a strategy to identify and reject infeasible targets by learning a target feasibility evaluator. To ensure that the evaluator is robust and non-delusional, we adopted a design choice combining off-policy compatible learning rule, distributional architecture, and data augmentation based on hindsight relabeling. Attaching to a planning agent, the designed evaluator learns by observing the agent's interactions with the environment and the targets produced by its generator, without the need to change the agent or its generator. Our controlled experiments show significant reductions in delusional behaviors and performance improvements for various kinds of existing agents.
2024-10-11
NeurIPS.cc/2024/Workshop/SafeGenAi (poster)
doi.org
proceedings.mlr.press
Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links
Alex Fedorov
Eloy Geenjaar
Lei Wu
Tristan Sylvain
Thomas P. DeRamus
Margaux Luck
Maria Misiura
Girish Mittapalle
R. Devon Hjelm
Sergey M. Plis
Vince D. Calhoun
In recent years, deep learning approaches have gained significant attention in predicting brain disorders using neuroimaging data. However, … (see more)conventional methods often rely on single-modality data and supervised models, which provide only a limited perspective of the intricacies of the highly complex brain. Moreover, the scarcity of accurate diagnostic labels in clinical settings hinders the applicability of the supervised models. To address these limitations, we propose a novel self-supervised framework for extracting multiple representations from multimodal neuroimaging data to enhance group inferences and enable analysis without resorting to labeled data during pre-training. Our approach leverages Deep InfoMax (DIM), a self-supervised methodology renowned for its efficacy in learning representations by estimating mutual information without the need for explicit labels. While DIM has shown promise in predicting brain disorders from single-modality MRI data, its potential for multimodal data remains untapped. This work extends DIM to multimodal neuroimaging data, allowing us to identify disorder-relevant brain regions and explore multimodal links. We present compelling evidence of the efficacy of our multimodal DIM analysis in uncovering disorder-relevant brain regions, including the hippocampus, caudate, insula, - and multimodal links with the thalamus, precuneus, and subthalamus hypothalamus. Our self-supervised representations demonstrate promising capabilities in predicting the presence of brain disorders across a spectrum of Alzheimer's phenotypes. Comparative evaluations against state-of-the-art unsupervised methods based on autoencoders, canonical correlation analysis, and supervised models highlight the superiority of our proposed method in achieving improved classification performance, capturing joint information, and interpretability capabilities. The computational efficiency of the decoder-free strategy enhances its practical utility, as it saves compute resources without compromising performance. This work offers a significant step forward in addressing the challenge of understanding multimodal links in complex brain disorders, with potential applications in neuroimaging research and clinical diagnosis.
2023-12-31
NeuroImage (published)
doi.org
CMIM: Cross-Modal Information Maximization For Medical Imaging
Tristan Sylvain
Francis Dutil
Tess Berthier
Lisa Di Jorio
Margaux Luck
R Devon Hjelm
Yoshua Bengio
In hospitals, data are siloed to specific information systems that make the same information available under different modalities such as th… (see more)e different medical imaging exams the patient undergoes (CT scans, MRI, PET, Ultrasound, etc.) and their associated radiology reports. This offers unique opportunities to obtain and use at train-time those multiple views of the same information that might not always be available at test-time.In this paper, we propose an innovative framework that makes the most of available data by learning good representations of a multi-modal input that are resilient to modality dropping at test-time, using recent advances in mutual information maximization. By maximizing cross-modal information at train time, we are able to outperform several state-of-the-art baselines in two different settings, medical image classification, and segmentation. In particular, our method is shown to have a strong impact on the inference-time performance of weaker modalities.
2021-06-05
IEEE International Conference on Acoustics, Speech, and Signal Processing (published)
doi.org
Object-Centric Image Generation from Layouts
Tristan Sylvain
Pengchuan Zhang
Yoshua Bengio
R Devon Hjelm
Shikhar Sharma
2021-05-17
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Exploring the Wasserstein metric for survival analysis
Tristan Sylvain
Margaux Luck
Joseph Paul Cohen
Heloise Cardinal
Andrea Lodi
Yoshua Bengio
2020-12-31
SPACA (published)
proceedings.mlr.press
proceedings.mlr.press
Cross-Modal Information Maximization for Medical Imaging: CMIM
Tristan Sylvain
Francis Dutil
Tess Berthier
Lisa Di Jorio
Margaux Luck
R Devon Hjelm
Yoshua Bengio
2020-10-19
ArXiv (preprint)
arxiv.org
arxiv.org
Image-to-image Mapping with Many Domains by Sparse Attribute Transfer
Matthew Amodio
Rim Assouel
Victor Schmidt
Tristan Sylvain
Smita Krishnaswamy
Yoshua Bengio
2020-06-22
ArXiv (preprint)
arxiv.org
arxiv.org
Joint Learning of Generative Translator and Classifier for Visually Similar Classes
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 wh… (see more)ere classes are visually similar and data is scarce. For this purpose, we propose joint learning from a scratch to train a classifier and a generative stochastic translation network end-to-end. The translation network is used to perform on-line data augmentation across classes, whereas previous works have mostly involved domain adaptation. To help the model further benefit from this data-augmentation, we introduce an adaptive fade-in loss and a quadruplet loss. We perform experiments on multiple datasets to demonstrate the proposed method’s performance in varied settings. Of particular interest, training on 40% of the dataset is enough for our model to surpass the performance of baselines trained on the full dataset. When our architecture is trained on the full dataset, we achieve comparable performance with state-of-the-art methods despite using a light-weight architecture.
2019-12-31
IEEE Access (published)
doi.org
arxiv.org
Learning to rank for censored survival data
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. Utilizing t… (see more)his information is a challenge because it is not obvious how to correctly incorporate these censored examples into a model. We study how three categories of loss functions, namely partial likelihood methods, rank methods, and our classification method based on a Wasserstein metric (WM) and the non-parametric Kaplan Meier estimate of the probability density to impute the labels of censored examples, can take advantage of this information. The proposed method allows us to have a model that predict the probability distribution of an event. If a clinician had access to the detailed probability of an event over time this would help in treatment planning. For example, determining if the risk of kidney graft rejection is constant or peaked after some time. Also, we demonstrate that this approach directly optimizes the expected C-index which is the most common evaluation metric for ranking survival models.
2018-06-05
ArXiv (preprint)
arxiv.org
arxiv.org
Deep Learning for Patient-Specific Kidney Graft Survival Analysis
Margaux Luck
Tristan Sylvain
Heloise 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 ca… (see more)re of patients. In this paper, we propose a deep learning method that directly models the survival function instead of estimating the hazard function to predict survival times for graft patients based on the principle of multi-task learning. By learning to jointly predict the time of the event, and its rank in the cox partial log likelihood framework, our deep learning approach outperforms, in terms of survival time prediction quality and concordance index, other common methods for survival analysis, including the Cox Proportional Hazards model and a network trained on the cox partial log-likelihood.
2017-05-28
ArXiv (preprint)
arxiv.org
arxiv.org
Diet Networks: Thin Parameters for Fat Genomics
Adriana Romero
Pierre Luc Carrier
Akram Erraqabi
Tristan Sylvain
Alex Auvolat
Étienne Dejoie
Marc-André Legault
Marie-Pierre Dubé
Julie G. Hussin
Yoshua Bengio
Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude… (see more) larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer: each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. 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). We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.
2016-12-31
ICLR.cc/2017/conference (poster)
doi.org
openreview.net