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 …
Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges
Continual learning (CL) is a particular machine learning paradigm where the data
distribution and learning objective change through time, or where all the training data and …
distribution and learning objective change through time, or where all the training data and …
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 …
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} …
[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 …
Remind your neural network to prevent catastrophic forgetting
People learn throughout life. However, incrementally updating conventional neural networks
leads to catastrophic forgetting. A common remedy is replay, which is inspired by how the …
leads to catastrophic forgetting. A common remedy is replay, which is inspired by how the …
Continuous learning in single-incremental-task scenarios
D Maltoni, V Lomonaco - Neural Networks, 2019 - Elsevier
It was recently shown that architectural, regularization and rehearsal strategies can be used
to train deep models sequentially on a number of disjoint tasks without forgetting previously …
to train deep models sequentially on a number of disjoint tasks without forgetting previously …
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 …
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 …
Imbalanced continual learning with partitioning reservoir sampling
Continual learning from a sequential stream of data is a crucial challenge for machine
learning research. Most studies have been conducted on this topic under the single-label …
learning research. Most studies have been conducted on this topic under the single-label …