Search Results for author:
Found 11 papers, 3 papers with code
On self-supervised multi-modal representation learning: An application to Alzheimer's disease
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.
Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's Disease
Sensory input from multiple sources is crucial for robust and coherent human perception.
Zero-Shot Learning from scratch (ZFS): leveraging local compositional representations
Zero-shot classification is a generalization task where no instance from the target classes is seen during training.
Cross-Modal Information Maximization for Medical Imaging: CMIM
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-to-image Mapping with Many Domains by Sparse Attribute Transfer
Unsupervised image-to-image translation consists of learning a pair of mappings between two domains without known pairwise correspondences between points.
Object-Centric Image Generation from Layouts
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.
Ranked #1 on
Layout-to-Image Generation
on COCO-Stuff 256x256
Locality and compositionality in zero-shot learning
In this work we study locality and compositionality in the context of learning representations for Zero Shot Learning (ZSL).
Joint Learning of Generative Translator and Classifier for Visually Similar Classes
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.
Learning to rank for censored survival data
Survival analysis is a type of semi-supervised ranking task where the target output (the survival time) is often right-censored.
Deep Learning for Patient-Specific Kidney Graft Survival Analysis
An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients.
Diet Networks: Thin Parameters for Fat Genomics
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).
