4 papers
Plasticity Loss in Deep Reinforcement Learning: A Survey
Timo Klein, Christoph Luther, Manus McAuliffe +3
Plasticity refers to a network's ability to adapt to changing data distributions, which is crucial for the successful training of deep reinforcement learning agents. Loss of plasti…
Understanding and Improving Hyperbolic Deep Reinforcement Learning
Timo Klein, Thomas Lang, Andrii Shkabrii +6
The exponential volume growth of hyperbolic geometry can embed the hierarchical relationships between states in reinforcement learning (RL) with far less distortion than Euclidean…
A geometric framework for momentum-based optimizers for low-rank training
Steffen Schotthöfer, Timon Klein, Jonas Kusch
Low-rank pre-training and fine-tuning have recently emerged as promising techniques for reducing the computational and storage costs of large neural networks. Training low-rank par…
Breaking the Reclustering Barrier in Centroid-based Deep Clustering
Lukas Miklautz, Timo Klein, Kevin Sidak +5
This work investigates an important phenomenon in centroid-based deep clustering (DC) algorithms: Performance quickly saturates after a period of rapid early gains. Practitioners c…