Artificial intelligence research has seen enormous progress over the past few decades, but it predominantly relies on fixed datasets and stationary environments. Continual learning is an …
Continual learning (CL) is a particular machine learning paradigm where the data distribution and learning objective change through time, or where all the training data and …
Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is …
A Heng, H Soh - Advances in Neural Information Processing …, 2024 - proceedings.neurips.cc
The recent proliferation of large-scale text-to-image models has led to growing concerns that such models may be misused to generate harmful, misleading, and inappropriate content …
M Farajtabar, N Azizan, A Mott… - … Conference on Artificial …, 2020 - proceedings.mlr.press
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 …
Deep neural networks are used in many state-of-the-art systems for machine perception. Once a network is trained to do a specific task, eg, bird classification, it cannot easily be …
Continual learning aims to improve the ability of modern learning systems to deal with non- stationary distributions, typically by attempting to learn a series of tasks sequentially. Prior art …
Replay is the reactivation of one or more neural patterns that are similar to the activation patterns experienced during past waking experiences. Replay was first observed in …
Deep neural networks (DNNs) have a wide range of applications, and software employing them must be thoroughly tested, especially in safety-critical domains. However, traditional …