115 citations · 153 across the 3 of their papers we have counts for
3 papers
Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape
Johanni Brea, Berfin Simsek, Bernd Illing +1
The permutation symmetry of neurons in each layer of a deep neural network gives rise not only to multiple equivalent global minima of the loss function, but also to first-order sa…
Biologically plausible deep learning -- but how far can we go with shallow networks?
Bernd Illing, Wulfram Gerstner, Johanni Brea
Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate model…
Algorithmic Composition of Melodies with Deep Recurrent Neural Networks
Florian Colombo, Samuel P. Muscinelli, Alexander Seeholzer +2
A big challenge in algorithmic composition is to devise a model that is both easily trainable and able to reproduce the long-range temporal dependencies typical of music. Here we i…