5 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Optimizers Qualitatively Alter Solutions And We Should Leverage This
Razvan Pascanu, Clare Lyle, Ionut-Vlad Modoranu +6
Due to the nonlinear nature of Deep Neural Networks (DNNs), one can not guarantee convergence to a unique global minimum of the loss when using optimizers relying only on local inf…
cs.LG2024★ 1 cited
Normalization and effective learning rates in reinforcement learning
Clare Lyle, Zeyu Zheng, Khimya Khetarpal +4
Normalization layers have recently experienced a renaissance in the deep reinforcement learning and continual learning literature, with several works highlighting diverse benefits…
cs.LG2024★ 5 cited
Disentangling the Causes of Plasticity Loss in Neural Networks
Clare Lyle, Zeyu Zheng, Khimya Khetarpal +4
Underpinning the past decades of work on the design, initialization, and optimization of neural networks is a seemingly innocuous assumption: that the network is trained on a \text…