Search Results for author: Claudia Clopath

Found 13 papers, 5 papers with code

When Does Re-initialization Work?

no code implementations20 Jun 2022 Sheheryar Zaidi, Tudor Berariu, Hyunjik Kim, Jörg Bornschein, Claudia Clopath, Yee Whye Teh, Razvan Pascanu

However, when deployed alongside other carefully tuned regularization techniques, re-initialization methods offer little to no added benefit for generalization, although optimal generalization performance becomes less sensitive to the choice of learning rate and weight decay hyperparameters.

Data Augmentation Image Classification

Maslow's Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation

1 code implementation18 May 2022 Sebastian Lee, Stefano Sarao Mannelli, Claudia Clopath, Sebastian Goldt, Andrew Saxe

Continual learning - learning new tasks in sequence while maintaining performance on old tasks - remains particularly challenging for artificial neural networks.

Continual Learning

A study on the plasticity of neural networks

no code implementations31 May 2021 Tudor Berariu, Wojciech Czarnecki, Soham De, Jorg Bornschein, Samuel Smith, Razvan Pascanu, Claudia Clopath

One aim shared by multiple settings, such as continual learning or transfer learning, is to leverage previously acquired knowledge to converge faster on the current task.

Continual Learning Transfer Learning

Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece

no code implementations12 May 2021 Luke Y. Prince, Roy Henha Eyono, Ellen Boven, Arna Ghosh, Joe Pemberton, Franz Scherr, Claudia Clopath, Rui Ponte Costa, Wolfgang Maass, Blake A. Richards, Cristina Savin, Katharina Anna Wilmes

We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent neural networks.

Spectral Normalisation for Deep Reinforcement Learning: an Optimisation Perspective

1 code implementation11 May 2021 Florin Gogianu, Tudor Berariu, Mihaela Rosca, Claudia Clopath, Lucian Busoniu, Razvan Pascanu

We conduct ablation studies to disentangle the various effects normalisation has on the learning dynamics and show that is sufficient to modulate the parameter updates to recover most of the performance of spectral normalisation.


Continual Reinforcement Learning with Multi-Timescale Replay

1 code implementation16 Apr 2020 Christos Kaplanis, Claudia Clopath, Murray Shanahan

In this paper, we propose a multi-timescale replay (MTR) buffer for improving continual learning in RL agents faced with environments that are changing continuously over time at timescales that are unknown to the agent.

Continual Learning Continuous Control +1

Cortical credit assignment by Hebbian, neuromodulatory and inhibitory plasticity

no code implementations1 Nov 2019 Johnatan Aljadeff, James D'amour, Rachel E. Field, Robert C. Froemke, Claudia Clopath

We propose that a combination of plasticity rules, 1) Hebbian, 2) acetylcholine-dependent and 3) noradrenaline-dependent excitatory plasticity, together with 4) inhibitory plasticity restoring E/I balance, effectively solves the credit assignment problem.

Neurons and Cognition Disordered Systems and Neural Networks

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

no code implementations20 Jul 2019 Amadeus Maes, Mauricio Barahona, Claudia Clopath

Learning to produce spatiotemporal sequences is a common task that the brain has to solve.

Policy Consolidation for Continual Reinforcement Learning

1 code implementation1 Feb 2019 Christos Kaplanis, Murray Shanahan, Claudia Clopath

We propose a method for tackling catastrophic forgetting in deep reinforcement learning that is \textit{agnostic} to the timescale of changes in the distribution of experiences, does not require knowledge of task boundaries, and can adapt in \textit{continuously} changing environments.

Continual Learning Continuous Control +1

A High GOPs/Slice Time Series Classifier for Portable and Embedded Biomedical Applications

no code implementations27 Feb 2018 Hamid Soleimani, Aliasghar, Makhlooghpour, Wilten Nicola, Claudia Clopath, Emmanuel. M. Drakakis

Such a trend in hardware design may not be efficient in applications where on-node computation is required and the focus is more on the area and power efficiency as in the case of portable and embedded biomedical devices.

Time Series

Continual Reinforcement Learning with Complex Synapses

no code implementations ICML 2018 Christos Kaplanis, Murray Shanahan, Claudia Clopath

Unlike humans, who are capable of continual learning over their lifetimes, artificial neural networks have long been known to suffer from a phenomenon known as catastrophic forgetting, whereby new learning can lead to abrupt erasure of previously acquired knowledge.

Continual Learning reinforcement-learning

Cannot find the paper you are looking for? You can Submit a new open access paper.