Abstract. Learning algorithms relying on Gibbs sampling based stochastic approximations of the log-likelihood gradient have become a common way to train Restricted Boltzmann Machin...
Abstract. We investigate the extent to which eye movements in natural dynamic scenes can be predicted with a simple model of bottom-up saliency, which learns on different visual re...
Eleonora Vig, Michael Dorr, Thomas Martinetz, Erha...
Structural equation models have been widely used to study causal relationships between continuous variables. Recently, a non-Gaussian method called LiNGAM was proposed to discover ...
We propose a model of the hippocampus aimed at learning the timed association between subsequent sensory events. The properties of the neural network allow it to learn and predict ...
Abstract. We examine efficacy of a classifier based on average of kernel density estimators; each estimator corresponds to a different data "resolution". Parameters of th...
Visual recognition faces the difficult problem of recognizing objects despite the multitude of their appearances. Ample neuroscientific evidence shows that the cortex uses a topogr...
Abstract. A common practice to gain invariant features in object recognition models is to aggregate multiple low-level features over a small neighborhood. However, the differences ...
So-called Physical Unclonable Functions are an emerging, new cryptographic and security primitive. They can potentially replace secret binary keys in vulnerable hardware systems an...
Training convolutional neural networks (CNNs) on large sets of high-resolution images is too computationally intense to be performed on commodity CPUs. Such architectures however ...