Self-supervised learning is a promising paradigm in deep learning that
e...
The recent breakthroughs in natural language processing for model pretra...
This paper demonstrates an approach for learning highly semantic image
r...
A successful paradigm in representation learning is to perform
self-supe...
We propose Masked Siamese Networks (MSN), a self-supervised learning
fra...
Popular approaches for minimizing loss in data-driven learning often inv...
This paper proposes a novel method of learning by predicting view assign...
Codistillation has been proposed as a mechanism to share knowledge among...
Motivated by large-scale optimization problems arising in the context of...
We investigate a strategy for improving the computational efficiency of
...
We study Nesterov's accelerated gradient method in the stochastic
approx...
Multi-simulator training has contributed to the recent success of Deep
R...
Large mini-batch parallel SGD is commonly used for distributed training ...
We consider a multi-agent framework for distributed optimization where e...