17 citations · 51 across the 31 of their papers we have counts for
33 papers
When Does Unsupervised Learning Succeed or Fail? A PoS Perspective on Reconstruction-Based Anomaly Detection
Mehmet Yamaç, Yagmur Mustu, Muhammad Numan Yousaf +2
Reconstruction-based unsupervised learning can fail in two opposing ways: a model may reconstruct anomalies too accurately or discard valid nominal variation. Using the Pursuit of…
EquiSELD: Efficient training of equivariant sound event localization and detection networks
Goksenin Yuksel, Marcel van Gerven, Kiki van der Heijden
First-order Ambisonics (FOA) signals exhibit exact O(3) symmetry: The rotation or reflection of the FOA signal modifies the direction of arrival of the sound sources, while preserv…
Bearings: Self-Supervised Soundfield Embeddings from First-Order Ambisonics
Goksenin Yuksel, Marcel van Gerven, Kiki van der Heijden
Recently proposed self-supervised audio encoders learn powerful general-purpose representations of sound scenes, yet they are spatially blind. To supply the missing spatial represe…
Feasibility of Time-Domain DNN-Based Speech Enhancement on Embedded FPGA for Hearing Aids
Feyisayo Olalere, Umut Altin, Kiki van der Heijden +1
Hearing aids impose strict latency and power constraints that current DNN-based speech enhancement systems struggle to meet on embedded hardware. We characterize this gap by deploy…
Tuning Agent-Based Predator-Prey Models Toward Lotka-Volterra Dynamics
Corinna Mandl, Siddharth Chaturvedi, Marcel van Gerven
Recent growth in compute power has made it increasingly feasible to use large-scale agent-based models to simulate complex adaptive systems. A central difficulty is that such model…
Axiomatizing Neural Networks via Pursuit of Subspaces
Mehmet Yamac, Mert Duman, Ugur Akpinar +4
While deep neural networks have achieved remarkable success across a wide range of domains, their underlying mechanisms remain poorly understood, and they are often regarded as bla…
Hidden States as Value Gradients: The Pontryagin Structure of Recurrent Policies
David Leeftink, Max Hinne, Marcel van Gerven
A key capability of intelligent agents is to act effectively under incomplete state observations. Recurrent policies address this by compressing observation histories into a hidden…
Role Differentiation in a Coupled Resource Ecology under Multi-Level Selection
Siddharth Chaturvedi, Ahmed El-Gazzar, Marcel van Gerven
A group of non-cooperating agents can succumb to the \emph{tragedy-of-the-commons} if all of them seek to maximize the same resource channel to improve their viability. In nature,…
Emergence of Internal State-Modulated Swarming in Multi-Agent Patch Foraging System
Siddharth Chaturvedi, Ahmed EL-Gazzar, Marcel van Gerven
Active particles are entities that sustain persistent out-of-equilibrium motion by consuming energy. Under certain conditions, they exhibit the tendency to self-organize through co…
Kozax: Flexible and Scalable Genetic Programming in JAX
Sigur de Vries, Sander W. Keemink, Marcel A. J. van Gerven
Genetic programming is an optimization algorithm inspired by evolution which automatically evolves the structure of interpretable computer programs. The fitness evaluation in genet…
Noise-based Local Learning using Stochastic Magnetic Tunnel Junctions
Kees Koenders, Leo Schnitzpan, Fabian Kammerbauer +6
Brain-inspired learning in physical hardware has enormous potential to learn fast at minimal energy expenditure. One of the characteristics of biological learning systems is their…
Ornstein-Uhlenbeck Adaptation as a Mechanism for Learning in Brains and Machines
Jesus Garcia Fernandez, Nasir Ahmad, Marcel van Gerven
Learning is a fundamental property of intelligent systems, observed across biological organisms and engineered systems. While modern intelligent systems typically rely on gradient…
Vibrational Control of Complex Networks
Yuzhen Qin, Fabio Pasqualetti, Danielle S. Bassett +1
The stability of complex networks, from power grids to biological systems, is crucial for their proper functioning. It is thus important to control such systems to maintain or rest…
Discovering Continuous-Time Memory-Based Symbolic Policies using Genetic Programming
Sigur de Vries, Sander Keemink, Marcel van Gerven
Artificial intelligence techniques are increasingly being applied to solve control problems, but often rely on black-box methods without transparent output generation. To improve t…
Subspace Node Pruning
Joshua Offergeld, Marcel van Gerven, Nasir Ahmad
Improving the efficiency of neural network inference is undeniably important in a time where commercial use of AI models increases daily. Node pruning is the art of removing comput…
Gradient-Free Training of Recurrent Neural Networks using Random Perturbations
Jesus Garcia Fernandez, Sander Keemink, Marcel van Gerven
Recurrent neural networks (RNNs) hold immense potential for computations due to their Turing completeness and sequential processing capabilities, yet existing methods for their tra…
Efficient Deep Learning with Decorrelated Backpropagation
Sander Dalm, Joshua Offergeld, Nasir Ahmad +1
The backpropagation algorithm remains the dominant and most successful method for training deep neural networks (DNNs). At the same time, training DNNs at scale comes at a signific…
Analytical Characterization of Epileptic Dynamics in a Bistable System
Yuzhen Qin, Ahmed El-Gazzar, Danielle S. Bassett +2
Epilepsy is one of the most common neurological disorders globally, affecting millions of individuals. Despite significant advancements, the precise mechanisms underlying this cond…
Universal Differential Equations as a Common Modeling Language for Neuroscience
Ahmed ElGazzar, Marcel van Gerven
The unprecedented availability of large-scale datasets in neuroscience has spurred the exploration of artificial deep neural networks (DNNs) both as empirical tools and as models o…
Neither hype nor gloom do DNNs justice
Felix A. Wichmann, Simon Kornblith, Robert Geirhos
Neither the hype exemplified in some exaggerated claims about deep neural networks (DNNs), nor the gloom expressed by Bowers et al. do DNNs as models in vision science justice: DNN…
Fixing the problems of deep neural networks will require better training data and learning algorithms
Drew Linsley, Thomas Serre
Bowers and colleagues argue that DNNs are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those…
Node Perturbation Can Effectively Train Multi-Layer Neural Networks
Sander Dalm, Marcel van Gerven, Nasir Ahmad
Backpropagation (BP) remains the dominant and most successful method for training parameters of deep neural network models. However, BP relies on two computationally distinct phase…
Beyond the limitations of any imaginable mechanism: large language models and psycholinguistics
Conor Houghton, Nina Kazanina, Priyanka Sukumaran
Large language models are not detailed models of human linguistic processing. They are, however, extremely successful at their primary task: providing a model for language. For thi…
How does artificial intelligence contribute to iEEG research?
Julia Berezutskaya, Anne-Lise Saive, Karim Jerbi +1
Artificial intelligence (AI) is a fast-growing field focused on modeling and machine implementation of various cognitive functions with an increasing number of applications in comp…