A comprehensive survey of continual learning: theory, method and application
To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …
Mechanisms of systems memory consolidation during sleep
Long-term memory formation is a major function of sleep. Based on evidence from
neurophysiological and behavioral studies mainly in humans and rodents, we consider the …
neurophysiological and behavioral studies mainly in humans and rodents, we consider the …
Dualprompt: Complementary prompting for rehearsal-free continual learning
Continual learning aims to enable a single model to learn a sequence of tasks without
catastrophic forgetting. Top-performing methods usually require a rehearsal buffer to store …
catastrophic forgetting. Top-performing methods usually require a rehearsal buffer to store …
Data distributional properties drive emergent in-context learning in transformers
Large transformer-based models are able to perform in-context few-shot learning, without
being explicitly trained for it. This observation raises the question: what aspects of the …
being explicitly trained for it. This observation raises the question: what aspects of the …
Learning to prompt for continual learning
The mainstream paradigm behind continual learning has been to adapt the model
parameters to non-stationary data distributions, where catastrophic forgetting is the central …
parameters to non-stationary data distributions, where catastrophic forgetting is the central …
[HTML][HTML] 2022 roadmap on neuromorphic computing and engineering
DV Christensen, R Dittmann… - Neuromorphic …, 2022 - iopscience.iop.org
Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …
science. In the von Neumann architecture, processing and memory units are implemented …
[HTML][HTML] Brain-inspired replay for continual learning with artificial neural networks
Artificial neural networks suffer from catastrophic forgetting. Unlike humans, when these
networks are trained on something new, they rapidly forget what was learned before. In the …
networks are trained on something new, they rapidly forget what was learned before. In the …
Adversarial reciprocal points learning for open set recognition
Open set recognition (OSR), aiming to simultaneously classify the seen classes and identify
the unseen classes as' unknown', is essential for reliable machine learning. The key …
the unseen classes as' unknown', is essential for reliable machine learning. The key …
Dualnet: Continual learning, fast and slow
Abstract According to Complementary Learning Systems (CLS) theory~\cite
{mcclelland1995there} in neuroscience, humans do effective\emph {continual learning} …
{mcclelland1995there} in neuroscience, humans do effective\emph {continual learning} …
Bridging biological and artificial neural networks with emerging neuromorphic devices: fundamentals, progress, and challenges
As the research on artificial intelligence booms, there is broad interest in brain‐inspired
computing using novel neuromorphic devices. The potential of various emerging materials …
computing using novel neuromorphic devices. The potential of various emerging materials …