Machine Learning Methods for Multi-Step Traffic Acquisition – Sparse-time classification (STR) has emerged as a promising tool for automatic vehicle identification. The main drawback of STR is its lack of training data and the difficulty of handling noisy data. In this work we present an innovative approach to the problem using Convolutional Neural Networks. In our model, we first use unsupervised learning as feature representation for image classification: the Convolutional Neural Network (CNN) is trained with an unlabeled image. The CNN learns a binary metric feature embedding representation of its output vectors (e.g., the k-dimensional). Following this representation, the CNN can model the training data by selecting a high-quality subset of the training data. Our method learns the representations and, by using the learned representations, can be used with the standard segmentation and classification algorithms in order to learn the feature representation for the given dataset. We evaluate our method on the challenging TIDA dataset and compare it to the state-of-the-arts.
The topic of machine learning has received a growing interest in the past years as it has many applications in both computer science and medicine. This paper presents a new method for a machine learning approach to learn latent state representations based on a deep neural network. Specifically, we propose a new method called a deep neural network model to learn a latent state representation from a vector in a recurrent neural network model. We further present a new way to learn a deep neural network based approach to latent state representation learning using a deep reinforcement learning algorithm (LSRL). The model is trained in a way to minimize the regret of the learned representation and predicts the output if it is better. Experiments on real data demonstrate the effectiveness of the proposed approach and demonstrate that the model outperforms previous state-of-the-art methods for the task.
Recurrent Neural Sequence-to-Sequence Models for Prediction of Adjective Outliers
Machine Learning Methods for Multi-Step Traffic Acquisition
Mining for Structured Shallow Activation Functions
Learning Latent Representations with Pairwise Sparse CodingThe topic of machine learning has received a growing interest in the past years as it has many applications in both computer science and medicine. This paper presents a new method for a machine learning approach to learn latent state representations based on a deep neural network. Specifically, we propose a new method called a deep neural network model to learn a latent state representation from a vector in a recurrent neural network model. We further present a new way to learn a deep neural network based approach to latent state representation learning using a deep reinforcement learning algorithm (LSRL). The model is trained in a way to minimize the regret of the learned representation and predicts the output if it is better. Experiments on real data demonstrate the effectiveness of the proposed approach and demonstrate that the model outperforms previous state-of-the-art methods for the task.