research
∙
09/05/2023
Probabilistic Self-supervised Learning via Scoring Rules Minimization
In this paper, we propose a novel probabilistic self-supervised learning...
share
research
∙
08/28/2023
Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning
Ensembling a neural network is a widely recognized approach to enhance m...
research
∙
05/25/2023
Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning
Learning quality document embeddings is a fundamental problem in natural...
research
∙
03/14/2023
Young Humans Make Change, Young Users Click: Creating Youth-Centered Networked Social Movements
From the urbanists' perspective, the everyday experience of young people...
research
∙
09/14/2022
Joint Debiased Representation and Image Clustering Learning with Self-Supervision
Contrastive learning is among the most successful methods for visual rep...
research
∙
09/13/2022
Evaluating User Experience in Literary and Film Geography-based Apps with a Cartographical User-Centered Design Lens
Geography scholarship currently includes interdisciplinary approaches an...
research
∙
09/06/2022
Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision
Learning from positive and unlabeled (PU) data is a setting where the le...
research
∙
01/31/2022
Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection
Pseudo-labeling solutions for positive-unlabeled (PU) learning have the ...
research
∙
09/22/2021
Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images
We propose a Deep Variational Clustering (DVC) framework for unsupervise...
research
∙
09/15/2021
Deep Bregman Divergence for Contrastive Learning of Visual Representations
Deep Bregman divergence measures divergence of data points using neural ...
research
∙
09/11/2021
Learning Statistical Representation with Joint Deep Embedded Clustering
One of the most promising approaches for unsupervised learning is combin...
research
∙
11/22/2018
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data
We propose a new generative adversarial architecture to mitigate imbalan...
research
∙
11/05/2018
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Gliomas are the most common primary brain malignancies, with different d...
research
∙
10/09/2018