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Found 6 papers, 2 papers with code
Supervised Contrastive Vision Transformer for Breast Histopathological Image Classification
We present a novel approach, Supervised Contrastive Vision Transformer (SupCon-ViT), for improving the classification of invasive ductal carcinoma in terms of accuracy and generalization by leveraging the inherent strengths and advantages of both transfer learning, i. e., pre-trained vision transformer, and supervised contrastive learning.
Highly Scalable Task Grouping for Deep Multi-Task Learning in Prediction of Epigenetic Events
There have been methods developed to address such negative transfer in other domains, such as computer vision.
Edge Attention-based Multi-Relational Graph Convolutional Networks
The proposed GCN model, which we call edge attention-based multi-relational GCN (EAGCN), jointly learns attention weights and node features in graph convolution.
VIGAN: Missing View Imputation with Generative Adversarial Networks
Especially, when certain samples miss an entire view of data, it creates the missing view problem.
A Sparse Interactive Model for Matrix Completion with Side Information
We prove that when the side features can span the latent feature space of the matrix to be recovered, the number of observed entries needed for an exact recovery is $O(\log N)$ where $N$ is the size of the matrix.
On Multiplicative Multitask Feature Learning
We prove that this framework is mathematically equivalent to the widely used multitask feature learning methods that are based on a joint regularization of all model parameters, but with a more general form of regularizers.
