26 citations · 70 across the 13 of their papers we have counts for
15 papers
16-bit Precision of Convolutional Neural Networks on Microcontroller Units for 8-bit Costs
Rui Liu, Benjamin Paaßen
To deploy deep neural networks on edge hardware, highly efficient inference schemes are necessary that retain high accuracy. This work presents W16A16, a high precision (16-bit), f…
Prototype Adaptation for Zero-Shot sEMG Movement Classification
Rui Liu, Benjamin Paassen
Surface electromyography (sEMG) enables the control of prostheses, allowing upper-limb amputees to re-gain some hand function. Most current research focuses on recognizing basic mo…
Aligning Generalisation Between Humans and Machines
Filip Ilievski, Barbara Hammer, Frank van Harmelen +22
Recent advances in AI -- including generative approaches -- have resulted in technology that can support humans in scientific discovery and forming decisions, but may also disrupt…
FairGLVQ: Fairness in Partition-Based Classification
Felix Störck, Fabian Hinder, Johannes Brinkrolf +3
Fairness is an important objective throughout society. From the distribution of limited goods such as education, over hiring and payment, to taxes, legislation, and jurisprudence.…
Fairness in KI-Systemen
Janine Strotherm, Alissa Müller, Barbara Hammer +1
The more AI-assisted decisions affect people's lives, the more important the fairness of such decisions becomes. In this chapter, we provide an introduction to research on fairness…
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…
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…
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…
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…
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…
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…
Tree Edit Distance Learning via Adaptive Symbol Embeddings: Supplementary Materials and Results
Benjamin Paaßen
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…
Expectation maximization transfer learning and its application for bionic hand prostheses
Benjamin Paaßen, Alexander Schulz, Janne Hahne +1
Machine learning models in practical settings are typically confronted with changes to the distribution of the incoming data. Such changes can severely affect the model performance…
The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces
Benjamin Paaßen, Barbara Hammer, Thomas William Price +3
Intelligent tutoring systems can support students in solving multi-step tasks by providing hints regarding what to do next. However, engineering such next-step hints manually or vi…
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces
Benjamin Paaßen, Christina Göpfert, Barbara Hammer
Graph models are relevant in many fields, such as distributed computing, intelligent tutoring systems or social network analysis. In many cases, such models need to take changes in…