4 citations · 4 across the 1 of their papers we have counts for
4 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…
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…
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…
Decorrelated Soft Actor-Critic for Efficient Deep Reinforcement Learning
Burcu KüçükoÄlu, Sander Dalm, Marcel van Gerven
The effectiveness of credit assignment in reinforcement learning (RL) when dealing with high-dimensional data is influenced by the success of representation learning via deep neura…