Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both content features of nodes and linkage patterns across graphs various applications. As real-world are often incomplete noisy, treating them as ground-truth information, which is a common practice most GCNs, unavoidably leads to sub-optimal solutions. Existing e...

Attention is a commonly used mechanism in sequence processing, but it of O(n^2) complexity which prevents its application to long sequences. The recently introduced neural Shuffle-Exchange network offers computation-efficient alternative, enabling the modelling long-range dependencies O(n log n) time. model, however, quite complex, involving sophisticated gating derived from Gated Recurrent Uni...

Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently. However, the capability of using point clouds with convolutional neural network has been so far not fully explored. In this paper, we present a convolutional neural network for semantic segmentation and object recognition with 3D point clouds. At the core of our network i...

Background: Deep learning has revolutionized artificial intelligence and has transformed many fields. It allows processing high-dimensional data (such as signals or images) without the need for feature engineering. The aim of this research is to develop a deep learning-based system to decode motor intent from electromyogram (EMG) signals. Methods: A myoelectric system based on convolutional ne...

The classification of various document images is considered an important step towards building a modern digital library or office automation system. Convolutional Neural Network (CNN) classifiers trained with backpropagation are considered to be the current state of the art model for this task. However, there are two major drawbacks for these classifiers: the huge computational power demand for...

Background: The most common types of non-melanoma skin cancer are basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). AKIEC -Actinic keratoses (Solar keratoses) and intraepithelial carcinoma (Bowen’s disease)- are common non-invasive precursors of SCC, which may progress to invasive SCC, if left untreated. Due to the importance of early detection in cancer treatment, this study aimed...

This paper presents an approach to forecast future locations of human hands and objects. Given an image frame, the goal is to predict presence and location of hands and objects in the future frame (e.g., 5 seconds later), even when they are not visible in the current frame. The key idea is that (1) an intermediate representation of a convolutional object recognition model abstracts scene inform...

A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is head...

Recently the ‘Million Song Dataset’, containing audio features and metadata for one million songs, was made available. In this paper, we build a convolutional network that is then trained to perform artist recognition, genre recognition and key detection. The network is tailored to summarize the audio features over musically significant timescales. It is infeasible to train the network on all a...