The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately,
many application domains do not have access to big data because acquiring data involves a …

Continual learning (CL) learns a sequence of tasks incrementally with the goal of achieving
two main objectives: overcoming catastrophic forgetting (CF) and encouraging knowledge …

Due to advancements in data collection, storage, and processing techniques, machine
learning has become a thriving and dominant paradigm. However, one of its main …

Existing research on continual learning of a sequence of tasks focused on dealing with
catastrophic forgetting, where the tasks are assumed to be dissimilar and have little shared …

Sentiment analysis is the computational study of people's opinions, sentiments, emotions,
moods, and attitudes. This fascinating problem offers numerous research challenges, but …

This paper studies continual learning (CL) of a sequence of aspect sentiment classification
(ASC) tasks. Although some CL techniques have been proposed for document sentiment …

This paper studies continual learning (CL) of a sequence of aspect sentiment classification
(ASC) tasks in a particular CL setting called domain incremental learning (DIL). Each task is …

Continual learning (CL) of a sequence of tasks is often accompanied with the catastrophic
forgetting (CF) problem. Existing research has achieved remarkable results in overcoming …

As there exist competitive subnetworks within a dense network in concert with Lottery Ticket
Hypothesis, we introduce a novel neuron-wise task incremental learning method, namely …

Continual Learning (CL) aims to learn a sequence of problems (ie, tasks and domains) by
transferring knowledge acquired on previous problems, whilst avoiding forgetting of past …