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 …

As a special case of machine learning, incremental learning can acquire useful knowledge
from incoming data continuously while it does not need to access the original data. It is …

This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing
ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the …

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 …

Fuzzing has become the de facto standard technique for finding software vulnerabilities.
However, even state-of-the-art fuzzers are not very efficient at finding hard-to-trigger …

In supervised machine learning, an agent is typically trained once and then deployed. While
this works well for static settings, robots often operate in changing environments and must …

Learning from non-stationary data streams, also called Task-Free Continual Learning
(TFCL) remains challenging due to the absence of explicit task information in most …

The problem of catastrophic forgetting occurs in deep learning models trained on multiple
databases in a sequential manner. Recently, generative replay mechanisms (GRM) have …

A unique cognitive capability of humans consists in their ability to acquire new knowledge
and skills from a sequence of experiences. Meanwhile, artificial intelligence systems are …

Humans can incrementally learn to do new visual detection tasks, which is a huge challenge
for today's computer vision systems. Incrementally trained deep learning models lack …