69 citations · 167 across the 14 of their papers we have counts for
15 papers
Brain-inspired learning in artificial neural networks: a review
Samuel Schmidgall, Jascha Achterberg, Thomas Miconi +4
Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation,…
Procedural generation of meta-reinforcement learning tasks
Thomas Miconi
Open-endedness stands to benefit from the ability to generate an infinite variety of diverse, challenging environments. One particularly interesting type of challenge is meta-learn…
Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning
Thomas Miconi
A hallmark of intelligence is the ability to autonomously learn new flexible, cognitive behaviors - that is, behaviors where the appropriate action depends not just on immediate st…
Hebbian learning with gradients: Hebbian convolutional neural networks with modern deep learning frameworks
Thomas Miconi
Deep learning networks generally use non-biological learning methods. By contrast, networks based on more biologically plausible learning, such as Hebbian learning, show comparativ…
Enabling Continual Learning with Differentiable Hebbian Plasticity
Vithursan Thangarasa, Thomas Miconi, Graham W. Taylor
Continual learning is the problem of sequentially learning new tasks or knowledge while protecting previously acquired knowledge. However, catastrophic forgetting poses a grand cha…
Learning to Continually Learn
Shawn Beaulieu, Lapo Frati, Thomas Miconi +4
Continual lifelong learning requires an agent or model to learn many sequentially ordered tasks, building on previous knowledge without catastrophically forgetting it. Much work ha…