6 citations · 17 across the 19 of their papers we have counts for
20 papers
Multi-modal transformer for signal classification in nanopore blockade experiments
Sandro Kuppel, Julian Hoßbach, Samuel Tovey +1
Nanopore devices have emerged as powerful tools for single-molecule sensing, with potential for rapid, portable diagnostics. They detect changes in ionic current as analytes enter…
From Rubble Simulation to Active Magnetic Mapping: Quantum Sensing for Disaster Response
Samuel Tovey, Stefan Prestel, Hiroshi Yamauchi
Locating survivors of building collapses within the first 72 hours is a critical challenge in disaster response, and existing sensing modalities provide only partial information ab…
Reinforcement Learning Enables Autonomous Microrobot Navigation and Intervention in Simulated Blood Capillaries
Jannik Drotleff, Samuel Tovey, Paul Hohenberger +4
Autonomous microrobots navigating biological vasculature could enable targeted drug delivery and thrombolysis, yet training control policies for realistic environments remains an o…
Sequential Spatiotemporal Magnetic-Field Reconstruction via Quantum Hamiltonian Learning with NV-Center Spin-1 Hamiltonians
Hiroshi Yamauchi, Sophie Colleen Stearn, Samuel Tovey
We propose a quantum-Hamiltonian-learning-based sequential reconstruction framework for dynamic two-dimensional magnetic-field maps using a local likelihood model derived from a ni…
Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail
Konstantin Nikolaou, Jonas Scheunemann, Sven Krippendorf +2
Neural scaling laws describe predictable power-law relationships between model size, dataset size, compute, and performance. While these laws guide the development of modern founda…
Reservoir computing from collective dynamics of active colloidal oscillators
Veit-Lorenz Heuthe, Lukas Seemann, Samuel Tovey +1
Physical reservoir computing is a computational framework that offers an energy- and computation-efficient alternative to conventional training of neural networks. In reservoir com…
Deep Learning-Driven Peptide Classification in Biological Nanopores
Julian Hoßbach, Samuel Tovey, Sandro Kuppel +3
Nanopore-based single-molecule sensing is a promising route to fast, low-cost disease diagnosis and protein sequencing: as an analyte such as a peptide or protein traverses a nanos…
Beyond Scaling Curves: Internal Dynamics of Neural Networks Through the NTK Lens
Konstantin Nikolaou, Sven Krippendorf, Samuel Tovey +1
Scaling laws offer valuable insights into the relationship between neural network performance and computational cost, yet their underlying mechanisms remain poorly understood. In t…
Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning
Tobias Fellner, David Kreplin, Samuel Tovey +1
Variational quantum machine learning algorithms have been proposed as promising tools for time series prediction, with the potential to handle complex sequential data more effectiv…
Collective variables of neural networks: empirical time evolution and scaling laws
Samuel Tovey, Sven Krippendorf, Michael Spannowsky +2
This work presents a novel means for understanding learning dynamics and scaling relations in neural networks. We show that certain measures on the spectrum of the empirical neural…
Peptide Classification from Statistical Analysis of Nanopore Translocation Experiments
Julian Hoßbach, Samuel Tovey, Tobias Ensslen +2
Protein characterization using nanopore-based devices promises to be a breakthrough method in basic research, diagnostics, and analytics. Current research includes the use of machi…
SwarmRL: Building the Future of Smart Active Systems
Samuel Tovey, Christoph Lohrmann, Tobias Merkt +6
This work introduces SwarmRL, a Python package designed to study intelligent active particles. SwarmRL provides an easy-to-use interface for developing models to control microscopi…
Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning
Samuel Tovey, Christoph Lohrmann, Christian Holm
Reinforcement learning (RL) is a flexible and efficient method for programming micro-robots in complex environments. Here we investigate whether reinforcement learning can provide…
Generating Quantum Reservoir State Representations with Random Matrices
Samuel Tovey, Tobias Fellner, Christian Holm +1
We demonstrate a novel approach to reservoir computation measurements using random matrices. We do so to motivate how atomic-scale devices could be used for real-world computationa…
Emergence of Accurate Atomic Energies from Machine Learned Noble Gas Potentials
Frank Uhlig, Samuel Tovey, Christian Holm
The quantum theory of atoms in molecules (QTAIM) gives access to well-defined local atomic energies. Due to their locality, these energies are potentially interesting in fitting at…
ZnTrack -- Data as Code
Fabian Zills, Moritz Schäfer, Samuel Tovey +2
The past decade has seen tremendous breakthroughs in computation and there is no indication that this will slow any time soon. Machine learning, large-scale computing resources, an…
Generating Minimal Training Sets for Machine Learned Potentials
Jan Finkbeiner, Samuel Tovey, Christian Holm
This letter presents a novel approach for identifying uncorrelated atomic configurations from extensive data sets with a non-standard neural network workflow known as random networ…
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning
Samuel Tovey, David Zimmer, Christoph Lohrmann +5
Multi-Agent Reinforcement Learning (MARL) is a promising candidate for realizing efficient control of microscopic particles, of which micro-robots are a subset. However, the micros…
Towards a Phenomenological Understanding of Neural Networks: Data
Samuel Tovey, Sven Krippendorf, Konstantin Nikolaou +1
A theory of neural networks (NNs) built upon collective variables would provide scientists with the tools to better understand the learning process at every stage. In this work, we…
Efficient Data Selection Methods for the Development of Machine Learned Potentials
Jan Finkbeiner, Samuel Tovey, Christian Holm
We present an investigation into data selection methods for the efficient sampling of configuration space as applied to the development of inter-atomic potentials for scale bridgin…