In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth completion from sparse laser scan data. First, we show that traditional convolutional networks perform poorly when applied to sparse data even when the location of missing data is provided to the network. To overcome this problem, we propose a simple yet effective sparse convolution ...

We present a model to perform authorship attribution of tweets using Convolutional Neural Networks (CNNs) over character n-grams. We also present a strategy that improves model interpretability by estimating the importance of input text fragments in the predicted classification. The experimental evaluation shows that text CNNs perform competitively and are able to outperform previous methods.

We report on the production of Carbon Nano Networks (CNNs) from dense microemulsions in which catalyst nanoparticles have been synthesized. CNNs are 3D carbon networks, consisting of branches and junctions, and are mesoporous, graphitic, and conductive being suitable as electrode materials.

Deep neural network algorithms are difficult to analyze because they lack structure allowing to understand the properties of underlying transforms and invariants. Multiscale Hierarchical Convolutional Networks are a theoretical class of structured deep convolutional networks that constitute a framework to understand neural network classification properties. However, a naive implementation of su...

Convolutional neural networks (CNNs) are seen to be extremely effective in many large object recognition tasks. One of the reasons for this is that they learn appropriate features also from the training data. The convolutional layers of a CNN have these feature generating filters whose weights are learnt. However, this entails learning millions of weights (across different layers) and hence lea...

The area of face recognition is one the most widely researched areas in domain computer vision and biometric. This because non-intrusive nature biometric makes it comparatively more suitable for application surveillance at public places such as airports. primitive methods could not give very satisfactory performance. However, with advent machine deep learning their recognition, several major br...

This paper proposes a generic method to learn interpretable convolutional filters in deep neural network (CNN) for object classification, where each filter encodes features of specific part. Our does not require additional annotations parts or textures supervision. Instead, we use the same training data as traditional CNNs. automatically assigns high conv-layer with an part certain category dur...

The computation of good image descriptors is key to the instance retrieval problem and has been the object of much recent interest from the multimedia research community. With deep learning becoming the dominant approach in computer vision, the use of representations extracted from Convolutional Neural Nets (CNNs) is quickly gaining ground on Fisher Vectors (FVs) as favoured state-of-the-art gl...

In recent years, brain tumors become the leading cause of death in the world. Detection and rapid classification of this tumor are very important and may indicate the likely diagnosis and treatment strategy. In this paper, we propose deep learning techniques based on the combinations of pre-trained VGG-16 CNNs to classify three types of brain tumors (i.e., meningioma, glioma, and pituitary tumo...