292 citations · 770 across the 46 of their papers we have counts for
5 papers · 1 filter
On the Universal Representation Property of Spiking Neural Networks
Shayan Hundrieser, Philipp Tuchel, Insung Kong +1
Inspired by biology, spiking neural networks (SNNs) process information via discrete spikes over time, offering an energy-efficient alternative to the classical computing paradigm…
Statistical Guarantees for High-Dimensional Stochastic Gradient Descent
Jiaqi Li, Zhipeng Lou, Johannes Schmidt-Hieber +1
Stochastic Gradient Descent (SGD) and its Ruppert-Polyak averaged variant (ASGD) lie at the heart of modern large-scale learning, yet their theoretical properties in high-dimension…
Spike-timing-dependent Hebbian learning as noisy gradient descent
Niklas Dexheimer, Sascha Gaudlitz, Johannes Schmidt-Hieber
Hebbian learning is a key principle underlying learning in biological neural networks. We relate a Hebbian spike-timing-dependent plasticity rule to noisy gradient descent with res…
On the expressivity of deep Heaviside networks
Insung Kong, Juntong Chen, Sophie Langer +1
We show that deep Heaviside networks (DHNs) have limited expressiveness but that this can be overcome by including either skip connections or neurons with linear activation. We pro…
Ordinal Patterns Based Change Points Detection
Annika Betken, Giorgio Micali, Johannes Schmidt-Hieber
The ordinal patterns of a fixed number of consecutive values in a time series is the spatial ordering of these values. Counting how often a specific ordinal pattern occurs in a tim…