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With ever greater computational resources and more accessible software, deep neural networks have become ubiquitous across industry and academia.
Their remarkable ability to generalize to new samples defies the conventional view, which holds that complex, ...
The motivation for this work is to improve the performance of deep neural networks through the optimization of the individual activation functions. Since the latter results in an infinite-dimensional optimization problem, we resolve the ambiguity by search ...
The present invention concerns a method of classifying a media item into a user profile suitability class in a classification system comprising a set of pre-trained artificial neural networks. The method comprising: receiving (101) a media file; extracting ...
Classically, vision is seen as a cascade of local, feedforward computations. This framework has been tremendously successful, inspiring a wide range of ground-breaking findings in neuroscience and computer vision. Recently, feedforward Convolutional Neural ...
We propose to optimize the activation functions of a deep neural network by adding a corresponding functional regularization to the cost function. We justify the use of a second-order total-variation criterion. This allows us to derive a general represente ...
Language independent query-by-example spoken term detection (QbE-STD) is the problem of retrieving audio documents from an archive, which contain a spoken query provided by a user. This is usually casted as a hypothesis testing and pattern matching problem ...
Uncertainty in deep learning has recently received a lot of attention in research. While stateof- the-art neural networks have managed to break many benchmarks in terms of accuracy, it has been shown that by applying minor perturbations to the input data, ...
Neural networks have been traditionally considered robust in the sense that their precision degrades gracefully with the failure of neurons and can be compensated by additional learning phases. Nevertheless, critical applications for which neural networks ...
In this semester project we develop an automated system to rank panoramas captured by the EPFL Livecam according to how visually appealing they are. In other words, a system able to predict an average of the rating of human users for such panoramas. As a w ...
Objective. This work presents a first motor imagery-based, adaptive brain–computer interface (BCI) speller, which is able to exploit application-derived context for improved, simultaneous classifier adaptation and spelling. Online spelling experiments with ...
