5 citations · 9 across the 4 of their papers we have counts for
4 papers
Quantifying lottery tickets under label noise: accuracy, calibration, and complexity
Viplove Arora, Daniele Irto, Sebastian Goldt +1
Pruning deep neural networks is a widely used strategy to alleviate the computational burden in machine learning. Overwhelming empirical evidence suggests that pruned models retain…
The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions
Nishil Patel, Sebastian Lee, Stefano Sarao Mannelli +2
Reinforcement learning (RL) algorithms have proven transformative in a range of domains. To tackle real-world domains, these systems often use neural networks to learn policies dir…
Maslow's Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation
Sebastian Lee, Stefano Sarao Mannelli, Claudia Clopath +2
Continual learning - learning new tasks in sequence while maintaining performance on old tasks - remains particularly challenging for artificial neural networks. Surprisingly, the…
Continual Learning in the Teacher-Student Setup: Impact of Task Similarity
Sebastian Lee, Sebastian Goldt, Andrew Saxe
Continual learning-the ability to learn many tasks in sequence-is critical for artificial learning systems. Yet standard training methods for deep networks often suffer from catast…