This paper demonstrates the potential of convolutional neural networks (CNN) for detecting and classifying prosodic events on words, specifically pitch accents and phrase boundary tones, from frame-based acoustic features. Typical approaches use not only feature representations of the word in question but also its surrounding context. We show that adding position features indicating the current...

Digital pathology has advanced substantially over the last decade however tumor localization continues to be a challenging problem due to highly complex patterns and textures in the underlying tissue bed. The use of convolutional neural networks (CNNs) to analyze such complex images has been well adopted in digital pathology. However in recent years, the architecture of CNNs have altered with t...

Deep convolutional neural networks comprise a subclass of deep neural networks (DNN) with a constrained architecture that leverages the spatial and temporal structure of the domain they model. Convolutional networks achieve the best predictive performance in areas such as speech and image recognition by hierarchically composing simple local features into complex models. Although DNNs have been ...

Convolutional neural networks provide an eecient method to constrain the complexity of feedforward neural networks by weightsharing. This network topology has been applied in particular to image classiication when raw images are to be classi-ed without preprocessing. In this paper two variations of convolutional networks-Neocognitron and Neoperceptron-are compared with classiiers based on fully...

Convolutional rectifier networks, i.e. convolutional neural networks with rectified linear activation and max or average pooling, are the cornerstone of modern deep learning. However, despite their wide use and success, our theoretical understanding of the expressive properties that drive these networks is partial at best. On other hand, we have a much firmer grasp of these issues in the world ...

motivated by the Convolutional Neural Networks about digit recognition and ImageNet deep neural network by Krizhevsky et al. [1], I did this project on Guqin notation recognition, which classified reduced characters with positioned 1-10 (一 -十) in handwritten Chinese characters and translated to other music recording scores. I built a four-layer convolutional neural network using adjusted CaffeN...

One of the most important parts of environment perception is the detection of obstacles in the surrounding of the vehicle. To achieve that, several sensors like radars, LiDARs and cameras are installed in autonomous vehicles. The produced sensor data is fused to a general representation of the surrounding. In this thesis the dynamic occupancy grid map approach of Nuss et al. [37] is used while ...

We introduce a novel type of artificial neural network structure and training procedure that results in networks that are provably, quantitatively more robust to adversarial samples than classical, endto-end trained classifiers. The main idea of our approach is to force the network to make predictions on what the given instance of the class under consideration would look like and subsequently t...

In this work, we present an adaptation of the sequence-tosequence model for structured vision tasks. In this model, the output variables for a given input are predicted sequentially using neural networks. The prediction for each output variable depends not only on the input but also on the previously predicted output variables. The model is applied to spatial localization tasks and uses convolu...