activity
20182021
most citedDynamic fairness - Breaking vicious cycles in automatic decision making

9 citations · 12 across the 2 of their papers we have counts for

collaborators

7 papers

cs.NE2021

Reservoir Stack Machines

Benjamin Paaßen, Alexander Schulz, Barbara Hammer

Memory-augmented neural networks equip a recurrent neural network with an explicit memory to support tasks that require information storage without interference over long times. A…

cs.LG2020

Reservoir memory machines

Benjamin Paassen, Alexander Schulz

In recent years, Neural Turing Machines have gathered attention by joining the flexibility of neural networks with the computational capabilities of Turing machines. However, Neura…

cs.LG2019

Adversarial Edit Attacks for Tree Data

Benjamin Paaßen

Many machine learning models can be attacked with adversarial examples, i.e. inputs close to correctly classified examples that are classified incorrectly. However, most research o…

cs.LG20193 cited

Embeddings and Representation Learning for Structured Data

Benjamin Paaßen, Claudio Gallicchio, Alessio Micheli +1

Performing machine learning on structured data is complicated by the fact that such data does not have vectorial form. Therefore, multiple approaches have emerged to construct vect…

cs.LG20199 cited

Dynamic fairness - Breaking vicious cycles in automatic decision making

Benjamin Paaßen, Astrid Bunge, Carolin Hainke +2

In recent years, machine learning techniques have been increasingly applied in sensitive decision making processes, raising fairness concerns. Past research has shown that machine…

cs.LG2018

Tree Edit Distance Learning via Adaptive Symbol Embeddings

Benjamin Paaßen, Claudio Gallicchio, Alessio Micheli +1

Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points fr…