Large language models (LLMs) have led to a series of breakthroughs in na...
Pretrained large language models (LLMs) are strong in-context learners t...
This paper studies the curious phenomenon for machine learning models wi...
Federated learning (FL) has recently attracted increasing attention from...
We study the problem of distributed mean estimation and optimization und...
We propose a structured extension to bidirectional-context conditional
l...
Large Transformer models have achieved impressive performance in many na...
Much of the literature on differential privacy focuses on item-level pri...
Modern retrieval problems are characterised by training sets with potent...
In the Vision-and-Language Navigation (VLN) task, an agent with egocentr...
The computational cost of training with softmax cross entropy loss grows...
We consider the problem of retrieving the most relevant labels for a giv...
Distributed stochastic gradient descent is an important subroutine in
di...
Recurrent neural network (RNN) language models (LMs) and Long Short Term...