Augmented Reality at Scale Using Wavelets and Deep Belief Networks – The human mind is a very natural language. We can understand it by representing what we have seen as a natural language. In this paper we would like to study an algorithm for automatic reasoning using the word-word similarity to identify a topic with an appropriate number of concepts. We consider a topic for a specific dataset and use an algorithm to extract the topic by using a neural network. We first show how to get the concept number from an input corpus via an analogy between topic and semantic representation. Then we show how to learn topic clustering using a neural network. The problem is that the goal of clustering one topic into a cluster of similar topics is not always desirable, as it may lead to more expensive queries. We present a novel approach that can estimate the topic clustering using the word-word similarity. The network is trained on a dataset of thousands of labeled examples (words, sentences and images) of a category. In the experiments on synthetic and human datasets we show how our approach improves the task of determining the category of a dataset by a novel measure of similarity.

We develop a new model for estimating the distance between two vehicles, called BMRD. The model uses real-valued data on different dimensions, and can model how they differ. This model is a good choice for data analysis as it is simple to use and flexible enough for human. This paper presents a simple yet powerful method that can extract high-quality human-level features from BMRD. The model uses a convolutional neural network (CNN), in combination with a preprocessing step that takes the input data into account. The network is trained using a dataset of thousands of vehicles, and the resulting model is able to accurately predict the vehicle distance, which would be useful for speeding up vehicle detection. This dataset is of the first published work demonstrating our approach for BMRD which shows good results for the test set.

Theano: a powerful compressive n-gram generator

Interactive Online Learning

Augmented Reality at Scale Using Wavelets and Deep Belief Networks

Distributed Optimistic Sampling for Population Genetics

Evaluation of Deep Learning Methods for Accurate Vehicle Speed MatchingWe develop a new model for estimating the distance between two vehicles, called BMRD. The model uses real-valued data on different dimensions, and can model how they differ. This model is a good choice for data analysis as it is simple to use and flexible enough for human. This paper presents a simple yet powerful method that can extract high-quality human-level features from BMRD. The model uses a convolutional neural network (CNN), in combination with a preprocessing step that takes the input data into account. The network is trained using a dataset of thousands of vehicles, and the resulting model is able to accurately predict the vehicle distance, which would be useful for speeding up vehicle detection. This dataset is of the first published work demonstrating our approach for BMRD which shows good results for the test set.