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…
RiboUnmix: Learning Shared Translational Dynamics from Biased and Noisy Ribo-seq Measurements
Gabriele Martino, Denis Skibinski, Ivo L. Hofacker +1
Ribosome profiling (Ribo-seq) measures ribosome distributions along mRNAs, but observed occupancy profiles also contain experiment-specific distortions and stochastic variability.…
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…
FRInGe: Distribution-Space Integrated Gradients with Fisher--Rao Geometry
Gabriele Martino, Sebastian Tschiatschek
Gradient-based attribution methods are model-faithful and scalable, but Integrated Gradients (IG) can be brittle because explanations depend on heuristic baselines, straight-line p…
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…
Rule-Guided Reinforcement Learning Policy Evaluation and Improvement
Martin Tappler, Ignacio D. Lopez-Miguel, Sebastian Tschiatschek +1
We consider the challenging problem of using domain knowledge to improve deep reinforcement learning policies. To this end, we propose LEGIBLE, a novel approach, following a multi-…
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…