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Density-based Shape Matching – We explore the problem of accurately predicting the shape of a random point. Our aim in this work is to learn a mapping mechanism from a single image taken with the help of a high resolution RGB-D image. We generalize the mapping to a new feature vector of the target point […]

Flexible Policy Gradient for Dynamic Structural Equation Models – This paper presents a new framework for learning graph embeddings that considers the relationship between the local form of a distribution and the continuous form, e.g., the marginal distribution, of the distribution given by the graph. We prove that a general algorithm is feasible to solve […]

On the convergence of the gradient of the closest upper bound on the number of folds in convolutional neural networks – In this work, we propose a novel deep CNN model named FastCNN with Fully Convolutional Networks (FCNN-CNN1) and propose a novel method for training and inference of the CNN’s recurrent state layers. Fast CNN […]

RoboJam: A Large Scale Framework for Multi-Label Image Monolingual Naming – RoboJam is a platform for collaborative learning of robotic image objects over a small geographical area. It is also a platform to experiment with the use of a variety of natural images. Here, we present a new collaborative framework for the exploration of deep […]

End-to-End Learning of Interactive Video Game Scripts with Deep Recurrent Neural Networks – We show that, based on a deep neural network (DNN) model, the Atari 2600-inspired video game Atari 2600 can be learnt from non-linear video clips. This study shows that Atari 2600 can produce a video that is non-linear in time compared to […]

Interactively Transferring Multimodal Representations of Human Attention with Convolutional Neural Networks – We present the first neural models for the task of grasping. When using a human-computer interaction, there is a number of factors to consider, including the presence of object classes, visual features (e.g., object’s spatial and temporal attributes) of the objects and the […]

Deep Learning Models From Scratch: A Survey – Learning structured knowledge is a crucial component of any knowledge representation, such as a representation of knowledge or a knowledge base, where knowledge is defined by its relations with other parts of a knowledge. The learning of knowledge based on the constraints is referred to as the […]

Learning to Order Information in Deep Reinforcement Learning – We consider general deep-learning techniques for a task of finding a reward function. We show that using an external reward function (e.g. an agent) can be an effective way to learn to order a function (in this case, by taking action). We apply our method to […]

Learning Unsupervised Object Localization for 6-DoF Scene Labeling – The success of recent deep learning-based vision systems for object localization has led to the development of large-scale object localization systems. These systems are challenging in that the tasks are hard for humans to do and humans usually cannot track objects at all and most of […]

Learning to Generate Patches using Adversarial Neural Networks – In this paper, we present a new technique for automated and adversarial neural network classification. The technique consists in building a neural network representation that can be trained to classify the output of an adversarial network and its input inputs (i.e. outputs obtained from a training […]