Biological organisms learn from interactions with their environment throughout their lifetime.
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …

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 …

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 …

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 …