Search Results for author:
Found 17 papers, 8 papers with code
OPSurv: Orthogonal Polynomials Quadrature Algorithm for Survival Analysis
This paper introduces the Orthogonal Polynomials Quadrature Algorithm for Survival Analysis (OPSurv), a new method providing time-continuous functional outputs for both single and competing risks scenarios in survival analysis.
AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval
Following this, the chosen articles are subjected to zero-shot summarization to attain succinct context.
What Constitutes Good Contrastive Learning in Time-Series Forecasting?
Despite these advances, there remains a significant gap in understanding the impact of different SSCL strategies on time series forecasting performance, as well as the specific benefits that SSCL can bring.
Robust Reinforcement Learning Objectives for Sequential Recommender Systems
Attention-based sequential recommendation methods have shown promise in accurately capturing users' evolving interests from their past interactions.
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes
Coarse labels do not capture the long-tailed spectrum of brain disorder phenotypes, which leads to a loss of generalizability of the model that makes them less useful in diagnostic settings.
Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting
The performance of time series forecasting has recently been greatly improved by the introduction of transformers.
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 64x64
Generative Adversarial Network
Layout-to-Image Generation
+1
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).
