5 papers
SpikingGamma: Surrogate-Gradient Free and Temporally Precise Online Training of Spiking Neural Networks with Smoothed Delays
Roel Koopman, Sebastian Otte, Sander Bohté
Neuromorphic hardware implementations of Spiking Neural Networks (SNNs) promise energy-efficient, low-latency AI through sparse, event-driven computation. Yet, training SNNs under…
The Resurrection of the ReLU
CoÅku Can Horuz, Geoffrey Kasenbacher, Saya Higuchi +7
Modeling sophisticated activation functions within deep learning architectures has evolved into a distinct research direction. Functions such as GELU, SELU, and SiLU offer smooth g…
WARP-LCA: Efficient Convolutional Sparse Coding with Locally Competitive Algorithm
Geoffrey Kasenbacher, Felix Ehret, Gerrit Ecke +1
The locally competitive algorithm (LCA) can solve sparse coding problems across a wide range of use cases. Recently, convolution-based LCA approaches have been shown to be highly e…
Representation Learning of Multivariate Time Series using Attention and Adversarial Training
Leon Scharwächter, Sebastian Otte
A critical factor in trustworthy machine learning is to develop robust representations of the training data. Only under this guarantee methods are legitimate to artificially genera…
Balanced Resonate-and-Fire Neurons
Saya Higuchi, Sebastian Kairat, Sander M. Bohte +1
The resonate-and-fire (RF) neuron, introduced over two decades ago, is a simple, efficient, yet biologically plausible spiking neuron model, which can extract frequency patterns wi…