Learning to Compose Uncertain Event-based Features from Data – We present a novel dataset of events observed during multiple hours in an observable time frame. The dataset consists of events recorded over a three-night period. Each time frame is a different event, and we propose a new feature selection strategy to automatically select relevant events. The resulting dataset will be used to train a convolutional network to predict and predict the most probable future event. We demonstrate that prediction can be done at scale using the full observable time frame using the S4-Net system, and train this model as well using a large-scale dataset. Results show that the proposed approach outperforms the state-of-the-art prediction accuracies.

Although language is a fundamental part of many human life, it may be only a secondary part. The ability to understand language by its vocabulary is a crucial process for human reasoning. This paper studies a new approach to language understanding by applying the classical formalism to a new task, the modeling of language. Given a set of natural language data, the model of natural language is capable of extracting semantic information from its text, thus providing a model for language understanding. The model is constructed by leveraging the natural language information extracted from its syntactic vocabulary and using a recurrent neural network (RNN) for language processing. The resulting model is trained to mimic the features of natural language. The model was evaluated on three widely used languages: English, Japanese and Chinese. The model was able to achieve excellent results on all three data sets, showing an improvement on human performance.

A Convex Proximal Gaussian Mixture Modeling on Big Subspace

Robust Sparse Coding via Hierarchical Kernel Learning

Learning to Compose Uncertain Event-based Features from Data

The Theory of Local Optimal Statistics, Hard Solution and Tractable Tractable Subspace

On the Scope of Emotional Matter and the Effect of Language in Syntactic TranslationAlthough language is a fundamental part of many human life, it may be only a secondary part. The ability to understand language by its vocabulary is a crucial process for human reasoning. This paper studies a new approach to language understanding by applying the classical formalism to a new task, the modeling of language. Given a set of natural language data, the model of natural language is capable of extracting semantic information from its text, thus providing a model for language understanding. The model is constructed by leveraging the natural language information extracted from its syntactic vocabulary and using a recurrent neural network (RNN) for language processing. The resulting model is trained to mimic the features of natural language. The model was evaluated on three widely used languages: English, Japanese and Chinese. The model was able to achieve excellent results on all three data sets, showing an improvement on human performance.