How can we efficiently find very large numbers of clusters C in very large datasets N of
potentially high dimensionality D? Here we address the question by using a novel …

We propose a simple and efficient clustering method for high-dimensional data with a large
number of clusters. Our algorithm achieves high-performance by evaluating distances of …

One iteration of standard k-means (ie, Lloyd's algorithm) or standard EM for Gaussian
mixture models (GMMs) scales linearly with the number of clusters C, data points N, and …

We explore classifier training for data sets with very few labels. We investigate this task
using a neural network for nonnegative data. The network is derived from a hierarchical …

We derive a novel variational expectation maximization approach based on truncated
posterior distributions. Truncated distributions are proportional to exact posteriors within …

Biological and artificial neural networks (ANNs) represent input signals as patterns of neural
activity. In biology, neuromodulators can trigger important reorganizations of these neural …

Event-based cameras encode changes in a visual scene with high temporal precision and
low power consumption, generating millions of events per second in the process. Current …

We establish a link between evolutionary algorithms (EAs) and learning of probabilistic
generative models with binary hidden variables. Learning is formulated as approximate …

From image recognition to automated driving, machine learning nowadays is all around us
and impacts various aspects of our daily lives. This disruptive technology is rapidly evolving …

One particular important complaint by hearing-impaired listeners is their reduced “Cocktail
Party Effect”, ie their inability to understand speech in a noisy environment. There has been …