Biological underpinnings for lifelong learning machines
D Kudithipudi, M Aguilar-Simon, J Babb… - Nature Machine …, 2022 - nature.com
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 …
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …
[HTML][HTML] Continual lifelong learning with neural networks: A review
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 …
knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is …
A continual learning survey: Defying forgetting in classification tasks
Artificial neural networks thrive in solving the classification problem for a particular rigid task,
acquiring knowledge through generalized learning behaviour from a distinct training phase …
acquiring knowledge through generalized learning behaviour from a distinct training phase …
Measuring catastrophic forgetting in neural networks
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 …
Once a network is trained to do a specific task, eg, bird classification, it cannot easily be …
Replay in deep learning: Current approaches and missing biological elements
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 …
patterns experienced during past waking experiences. Replay was first observed in …
[PDF][PDF] Continual learning: A comparative study on how to defy forgetting in classification tasks
Artificial neural networks thrive in solving the classification problem for a particular rigid task,
where the network resembles a static entity of knowledge, acquired through generalized …
where the network resembles a static entity of knowledge, acquired through generalized …
[HTML][HTML] A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning
Current deep learning methods are regarded as favorable if they empirically perform well on
dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual …
dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual …
Continual learning via local module composition
O Ostapenko, P Rodriguez… - Advances in Neural …, 2021 - proceedings.neurips.cc
Modularity is a compelling solution to continual learning (CL), the problem of modeling
sequences of related tasks. Learning and then composing modules to solve different tasks …
sequences of related tasks. Learning and then composing modules to solve different tasks …
Continual learning for recurrent neural networks: an empirical evaluation
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 …
solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with …
The stability-plasticity dilemma: Investigating the continuum from catastrophic forgetting to age-limited learning effects
M Mermillod, A Bugaiska, P Bonin - Frontiers in psychology, 2013 - frontiersin.org
The stability-plasticity dilemma is a wellknow constraint for artificial and biological neural
systems. The basic idea is that learning in a parallel and distributed system requires …
systems. The basic idea is that learning in a parallel and distributed system requires …