In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large
language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine …

Federated learning has emerged as an important distributed learning paradigm, which
normally involves collaborative updating with others and local updating on private data …

Artificial neural networks thrive in solving the classification problem for a particular rigid task,
acquiring knowledge through generalized learning behaviour from a distinct training phase …

Computer vision models suffer from a phenomenon known as catastrophic forgetting when
learning novel concepts from continuously shifting training data. Typical solutions for this …

Neural networks are achieving state of the art and sometimes super-human performance on
learning tasks across a variety of domains. Whenever these problems require learning in a …

Abstract The continuing Machine Learning (ML) revolution indubitably has had a significant
positive impact on the analysis of downlinked satellite data. Other aspects of the Earth …

Continual Learning (CL) research typically focuses on tackling the phenomenon of
catastrophic forgetting in neural networks. Catastrophic forgetting is associated with an …

We introduce a new measure to evaluate the transferability of representations learned by
classifiers. Our measure, the Log Expected Empirical Prediction (LEEP), is simple and easy …

Catastrophic forgetting affects the training of neural networks, limiting their ability to learn
multiple tasks sequentially. From the perspective of the well established plasticity-stability …

Inspired by human learning, researchers have proposed ordering examples during training
based on their difficulty. Both curriculum learning, exposing a network to easier examples …