activity
20162026
most citedThe neuroconnectionist research programme

24 citations · 78 across the 32 of their papers we have counts for

collaborators
Showing 2025Show all

13 papers · 1 filter

cs.LG2025

Automated Discovery of Laser Dicing Processes with Bayesian Optimization for Semiconductor Manufacturing

David Leeftink, Roman Doll, Heleen Visserman +4

Laser dicing of semiconductor wafers is a critical step in microelectronic manufacturing, where multiple sequential laser passes precisely separate individual dies from the wafer.…

cs.LG2025

Generative Modeling of Clinical Time Series via Latent Stochastic Differential Equations

Muhammad Aslanimoghanloo, Ahmed ElGazzar, Marcel van Gerven

Clinical time series data from electronic health records and medical registries offer unprecedented opportunities to understand patient trajectories and inform medical decision-mak…

cs.LG2025

A Unified Perspective on Optimization in Machine Learning and Neuroscience: From Gradient Descent to Neural Adaptation

Jesús García Fernández, Nasir Ahmad, Marcel van Gerven

Iterative optimization is central to modern artificial intelligence (AI) and provides a crucial framework for understanding adaptive systems. This review provides a unified perspec…

cs.SD2025

WavJEPA: Semantic learning unlocks robust audio foundation models for raw waveforms

Goksenin Yuksel, Pierre Guetschel, Michael Tangermann +2

Learning audio representations from raw waveforms overcomes key limitations of spectrogram-based audio representation learning, such as the long latency of spectrogram computation…

cs.MA2025

ABMax: A JAX-based Agent-based Modeling Framework

Siddharth Chaturvedi, Ahmed El-Gazzar, Marcel van Gerven

Agent-based modeling (ABM) is a principal approach for studying complex systems. By decomposing a system into simpler, interacting agents, agent-based modeling (ABM) allows researc…

cs.LG2025

Real-Time Decorrelation-Based Anomaly Detection for Multivariate Time Series

Amirhossein Sadough, Mahyar Shahsavari, Mark Wijtvliet +1

Anomaly detection (AD) plays a vital role across a wide range of real-world domains by identifying data instances that deviate from expected patterns, potentially signaling critica…