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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 …
[HTML][HTML] Embracing change: Continual learning in deep neural networks
Artificial intelligence research has seen enormous progress over the past few decades, but it
predominantly relies on fixed datasets and stationary environments. Continual learning is an …
predominantly relies on fixed datasets and stationary environments. Continual learning is an …
Understanding the role of training regimes in continual learning
Catastrophic forgetting affects the training of neural networks, limiting their ability to learn
multiple tasks sequentially. From the perspective of the well established plasticity-stability …
multiple tasks sequentially. From the perspective of the well established plasticity-stability …
Linear mode connectivity in multitask and continual learning
SI Mirzadeh, M Farajtabar, D Gorur, R Pascanu… - ar** large foundation models up to date on latest data is inherently expensive. To avoid
the prohibitive costs of constantly retraining, it is imperative to continually train these models …
the prohibitive costs of constantly retraining, it is imperative to continually train these models …
Balancing stability and plasticity through advanced null space in continual learning
Continual learning is a learning paradigm that learns tasks sequentially with resources
constraints, in which the key challenge is stability-plasticity dilemma, ie, it is uneasy to …
constraints, in which the key challenge is stability-plasticity dilemma, ie, it is uneasy to …
Continual learning for natural language generation in task-oriented dialog systems
Natural language generation (NLG) is an essential component of task-oriented dialog
systems. Despite the recent success of neural approaches for NLG, they are typically …
systems. Despite the recent success of neural approaches for NLG, they are typically …
Ader: Adaptively distilled exemplar replay towards continual learning for session-based recommendation
Session-based recommendation has received growing attention recently due to the
increasing privacy concern. Despite the recent success of neural session-based …
increasing privacy concern. Despite the recent success of neural session-based …