Multi-modal contrastive representation (MCR) of more than three modalities is critical in multi-modal learning.

Educational recommendation lies in constructing a recommend system that can process the interactions between students and questions.

The focus of our work is on diagnostic tasks in personalized learning, such as cognitive diagnosis and knowledge tracing. The goal of these tasks is to assess students'

Many classic methods have shown non-local self-similarity in natural images to be an effective prior for image restoration. However, it remains unclear and challenging to make use of this intrinsic property via deep networks.

They exchange 0/1 spikes for processing information, thus most of the multiplications in networks can be replaced by additions.

First we propose a two-stage architecture for ourgenerator,consisting ofamask generator,andanimage in-painter which cooperate toachieve removal.

Recent progress hasdemonstrated thatsuchmeta-learning methods may exceed scalable human-invented architectures on image classification tasks.