305 citations · 611 across the 20 of their papers we have counts for
5 papers · 1 filter
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language
Jamie Fairbrother, Christopher Nemeth, Maxime Rischard +2
Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources. Key to applying Ga…
Learning to Generate Music with BachProp
Florian Colombo, Johanni Brea, Wulfram Gerstner
As deep learning advances, algorithms of music composition increase in performance. However, most of the successful models are designed for specific musical structures. Here, we pr…
On the choice of metric in gradient-based theories of brain function
Simone Carlo Surace, Jean-Pascal Pfister, Wulfram Gerstner +1
The idea that the brain functions so as to minimize certain costs pervades theoretical neuroscience. Since a cost function by itself does not predict how the brain finds its minima…
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation
Dane Corneil, Wulfram Gerstner, Johanni Brea
Modern reinforcement learning algorithms reach super-human performance on many board and video games, but they are sample inefficient, i.e. they typically require significantly mor…
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of neoHebbian Three-Factor Learning Rules
Wulfram Gerstner, Marco Lehmann, Vasiliki Liakoni +2
Most elementary behaviors such as moving the arm to grasp an object or walking into the next room to explore a museum evolve on the time scale of seconds; in contrast, neuronal act…