Abstract page for arXiv paper 1802.08435: Efficient Neural Audio Synthesis

Jan Novak

A technique for training very deep neural networks

Neural networks, architectures and training at depth

Nikolaos Kourentzes Forecasting research Skip to content Downloads About Category Archives: Refereed conference proceedings Data Driven Fitting Sample Selection For Time Series Forecasting With Neural Networks N. Kourentzes, 2012, International Joint Conference on Neural Networks, Brisbane, 10-15 June 2012. Modelling functional outliers for high frequency time series forecasting with neural networks: an empirical evaluation for electricity load data N. Kourentzes, 2011, International Conference on Data Mini

Communication is neural synchronization. Explore how language facilitates neural coupling and learn protocols to maximize clarity and influence

Algorithms off the convex path.

Photo by Ian Sane Last week I had a question from a colleague about reproducibility in TensorFlow, specifically in the 1.14 era. He wanted to be able to run the same training code multiple times and get exactly the same results, which on the surface doesn't seem like an unreasonable expectation. Machine learning training is…

The Data Exchange Podcast: Paolo Cremonesi and Maurizio Ferrari Dacrema on the reproducibility, complexity, and inefficiency of neural methods for recommenders. Subscribe: Apple • Android • Spotify • Stitcher • Google • RSS. This week’s guests are leading researchers in recommendation systems: Paolo Cremonesi is Professor of Computer Science and Maurizio Ferrari Dacrema is a Postdoc at Politecnico di Milano, where they are both part of

The biological brain neurons exhibit various critical phase transition patterns, among them is the long-range memory phenomenon. One common hypothesis is that t