Dependent Component Analysis: Estimating the sum of its components – Eddie is an open-source framework for analysis of probabilistic models. The framework is based on a special formulation of the joint expectation maximization problem and the maximum likelihood maximization problem. The framework is a combination of probability theory and data theory. The probabilistic models are constructed by applying the probability estimate and the maximum likelihood maximization as a set of functions of the joint likelihood estimate, as well as the maximum likelihood minimization problem using the statistical analysis of the joint likelihood estimate. The framework is built on top of a probabilistic model and a posterior distribution, and is an efficient framework for analysis through the joint expectation maximization and the maximum likelihood minimization problem. The framework is evaluated with the benchmark dataset, MNIST, comparing the performance of four supervised classification methods. The results obtained show that the framework can produce predictive results that are of higher quality than other alternatives.

In this thesis, we propose an efficient and robust method for human action detection from video. The first part is a supervised learning framework that is trained on a video set without an agent to automatically detect pedestrians. The first part is an implementation of this framework that uses semantic tagging to select a target image that can be used to automatically detect pedestrians. The second part is an implementation of the framework that is trained using deep neural network. The proposed framework is tested on four large public action datasets. The results show that we can accurately detect pedestrians from video by a method that is fast and robust. Experimental results show that we can distinguish pedestrians from vehicles by using the semantic tag.

Axiomatic Properties of Two-Stream Convolutional Neural Networks

# Dependent Component Analysis: Estimating the sum of its components

Learning Non-linear Structure from High-Order Interactions in Graphical Models

Semi-Automatic Detection of Pedestrians on HighwayIn this thesis, we propose an efficient and robust method for human action detection from video. The first part is a supervised learning framework that is trained on a video set without an agent to automatically detect pedestrians. The first part is an implementation of this framework that uses semantic tagging to select a target image that can be used to automatically detect pedestrians. The second part is an implementation of the framework that is trained using deep neural network. The proposed framework is tested on four large public action datasets. The results show that we can accurately detect pedestrians from video by a method that is fast and robust. Experimental results show that we can distinguish pedestrians from vehicles by using the semantic tag.