11 citations · 23 across the 8 of their papers we have counts for
8 papers
Probabilistic Prediction of Neural Dynamics via Autoregressive Flow Matching
Nicole Rogalla, Yuzhen Qin, Mario Senden +2
Forecasting neural activity in response to naturalistic stimuli remains a key challenge for understanding brain dynamics and enabling downstream neurotechnological applications. He…
Control of Discrete-Time Linear Systems with Charge-Balanced Inputs
Yuzhen Qin, Zonglin Liu, Marcel van Gerven
Electrical brain stimulation relies on externally applied currents to modulate neural activity, but safety constraints require each stimulation cycle to be charge-balanced, enforci…
Neuromorphic Intelligence
Marcel van Gerven
Neuromorphic computing seeks to replicate the remarkable efficiency, flexibility, and adaptability of the human brain in artificial systems. Unlike conventional digital approaches,…
Decorrelation Speeds Up Vision Transformers
Kieran Carrigg, Rob van Gastel, Melda Yeghaian +3
Masked Autoencoder (MAE) pre-training of vision transformers (ViTs) yields strong performance in low-label data regimes but comes with substantial computational costs, making it im…
Multimodal Integration of Longitudinal Noninvasive Diagnostics for Survival Prediction in Immunotherapy Using Deep Learning
Melda Yeghaian, Zuhir Bodalal, Daan van den Broek +4
Purpose: Immunotherapies have revolutionized the landscape of cancer treatments. However, our understanding of response patterns in advanced cancers treated with immunotherapy rema…
Foragax: An Agent-Based Modelling Framework Based on JAX
Siddharth Chaturvedi, Ahmed El-Gazzar, Marcel van Gerven
Foraging for resources is a ubiquitous activity conducted by living organisms in a shared environment to maintain their homeostasis. Modelling multi-agent foraging in-silico allows…
Wasserstein Variational Inference
Luca Ambrogioni, Umut Güçlü, Yağmur Güçlütürk +3
This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory. Wasserstein variational inference uses a ne…
GP CaKe: Effective brain connectivity with causal kernels
Luca Ambrogioni, Max Hinne, Marcel van Gerven +1
A fundamental goal in network neuroscience is to understand how activity in one region drives activity elsewhere, a process referred to as effective connectivity. Here we propose t…