Learning useful representations of recurrent neural network weight matrices
Recurrent Neural Networks are general-purpose computers. The program of an RNN is its weight matrix. How to learn useful representations of RNN weights that ...
Recurrent Neural Networks are general-purpose computers. The program of an RNN is its weight matrix. How to learn useful representations of RNN weights that ...
Our novel MusicSlots method adapts SlotAttention to the audio domain, to achieve unsupervised music decomposition.
We study goal-conditioned neural nets (NNs) that learn to generate deep NN policies in form of context-specific weight matrices, similar to Fast Weight Progr...
There are two important things in science–finding answers to given questions, and coming up with good questions. Our artificial scientists not only learn to ...
The act of telling stories is a fundamental part of what it means to be human. This work introduces the concept of narrative information, which we define to ...
Master thesis on generating classical music with the help of transformers. Written at the Bosch Center for Artificial Intelligence.
Real-time exploration of a sound processing neural network.
Recently I started to learn how to use d3.js, a JavaScript library for interactive data-driven visualizations. As a first little project, I decided to make i...
Derivation of the Kantorovich-Rubinstein duality for the use in Wasserstein Generative Adversarial Networks
Explanation of the most important properties of Daubechies wavelets and the algorithm to calculate them
Derivation of the dilation and wavelet equation from an implementation of the Fast Wavelet Transform
Dies ist ein kurzes Essay, das ich im Frühjahr 2016 zu einer vorgegebenen Fragestellung geschrieben habe: Werden Roboter jemals so werden wie wir? Und wolle...