4 citations · 4 across the 4 of their papers we have counts for
33 papers
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…
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…
Evolving Layer-Specific Scalar Functions for Hardware-Aware Transformer Adaptation
Kieran Carrigg, Sigur de Vries, Amirhossein Sadough +1
Vision Transformers (ViTs) achieve state-of-the-art performance on challenging vision tasks, but their deployment on edge devices is severely hindered by the computational complexi…
Neural Co-state Policies: Structuring Hidden States in Recurrent Reinforcement Learning
David Leeftink, Max Hinne, Marcel van Gerven
A key capability of intelligent agents is operating under partial observability: reasoning and acting effectively despite missing or incomplete state observations. While recurrent…
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…
Spiking neurons as predictive controllers of linear systems
Paolo Agliati, André Urbano, Pablo Lanillos +3
Neurons communicate with downstream systems via sparse and incredibly brief electrical pulses, or spikes. Using these events, they control various targets such as neuromuscular uni…