8 citations · 9 across the 9 of their papers we have counts for
11 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…
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…
H-SPLID: HSIC-based Saliency Preserving Latent Information Decomposition
Lukas Miklautz, Chengzhi Shi, Andrii Shkabrii +5
We introduce H-SPLID, a novel algorithm for learning salient feature representations through the explicit decomposition of salient and non-salient features into separate spaces. We…
An Introductory Survey to Autoencoder-based Deep Clustering -- Sandboxes for Combining Clustering with Deep Learning
Collin Leiber, Lukas Miklautz, Claudia Plant +1
Autoencoders offer a general way of learning low-dimensional, non-linear representations from data without labels. This is achieved without making any particular assumptions about…
Motif Discovery Framework for Psychiatric EEG Data Classification
Melanija Kraljevska, Katerina Hlavackova-Schindler, Lukas Miklautz +1
In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response due to the delayed noticeable ef…
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…
SHADE: Deep Density-based Clustering
Anna Beer, Pascal Weber, Lukas Miklautz +4
Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for current clustering methods. We introduce SHADE (Structure-preserving High-dimensional Analys…
Text-Guided Alternative Image Clustering
Andreas Stephan, Lukas Miklautz, Collin Leiber +4
Traditional image clustering techniques only find a single grouping within visual data. In particular, they do not provide a possibility to explicitly define multiple types of clus…
Text-Guided Image Clustering
Andreas Stephan, Lukas Miklautz, Kevin Sidak +4
Image clustering divides a collection of images into meaningful groups, typically interpreted post-hoc via human-given annotations. Those are usually in the form of text, begging t…
Deep Clustering With Consensus Representations
Lukas Miklautz, Martin Teuffenbach, Pascal Weber +4
The field of deep clustering combines deep learning and clustering to learn representations that improve both the learned representation and the performance of the considered clust…