3 citations · 3 across the 7 of their papers we have counts for
8 papers
Empirical Boundaries of Reservoir Computing for Few-Shot Sequence Pattern Tasks
Mario Mallea, Angela Nebot, Franscisco Mugica
Efficient sequence modeling remains a fundamental challenge in machine learning. Despite the success of Transformers, recurrent neural networks (RNNs) have recently re-emerged as e…
hyperbolix: Hyperbolic Deep Learning in JAX
Timo Klein, Thomas Lang, Yllka Velaj +1
We present hyperbolix, an open-source library for hyperbolic deep learning in JAX, built on Flax NNX. To our knowledge, it is the first comprehensive, general-purpose hyperbolic de…
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…
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…
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…
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
Shengyi Huang, Quentin Gallouédec, Florian Felten +30
In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curv…
Active Third-Person Imitation Learning
Timo Klein, Susanna Weinberger, Adish Singla +1
We consider the problem of third-person imitation learning with the additional challenge that the learner must select the perspective from which they observe the expert. In our set…