Learning continuously during all model lifetime is fundamental to deploy machine learning
solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with …

Artificial intelligence systems are increasingly being used in industrial applications, security
and military contexts, disaster response complexes, policing and justice practices, finance …

Continual learning (CL) aims to develop techniques by which a single model adapts to an
increasing number of tasks encountered sequentially, thereby potentially leveraging …

Existing work in continual learning (CL) focuses on mitigating catastrophic forgetting, ie,
model performance deterioration on past tasks when learning a new task. However, the …

The training of sparse neural networks is becoming an increasingly important tool for
reducing the computational footprint of models at training and evaluation, as well enabling …

The goal of continual learning (CL) is to learn different tasks over time. The main desiderata
associated with CL are to maintain performance on older tasks, leverage the latter to …

The success of deep ensembles on improving predictive performance, uncertainty
estimation, and out-of-distribution robustness has been extensively studied in the machine …

Existing research on task incremental learning in continual learning has primarily focused
on preventing catastrophic forgetting (CF). Although several techniques have achieved …

A new line of research for feature selection based on neural networks has recently emerged.
Despite its superiority to classical methods, it requires many training iterations to converge …

Modern machine learning systems need to be able to cope with constantly arriving and
changing data. Two main areas of research dealing with such scenarios are continual …