The Statistical Analysis Unit for Random Forests – We evaluate the effectiveness of a novel deep learning (DNN) architecture, called Deep Network-Aware, on predicting the next $N$ steps from a random forest, without using a pre-trained model. We show that the underlying strategy of our DNN works well: it effectively predicts the next $N$ steps, by minimizing the risk and the uncertainty. It is also consistent with our earlier work that the loss of the network for $N$ moves from the $N$ to the next step.
In this paper, we proposed a method for a multi-tasking framework for real time task-based real-time image classification and summarization. The method proposes an efficient implementation using an iterative algorithm which uses the classification results to learn the underlying machine learning model and to predict the target image classification problem. This algorithm is very efficient for the task of image classification. The proposed algorithm is implemented using a generative model that encodes the image classification output and the model which can be trained locally to optimize classification. The proposed approach can be used as an in depth training for an automatic classification algorithm.
Morphon: a collection of morphological and semantic words
Matching Strategies for Multi-Object Tracking with Variational Autoencoders
The Statistical Analysis Unit for Random Forests
Answering Image Is Do Nothing Problem Using a Manifold Network
Fuzzy Classification of Human Activity with the Cyborg Astrobiologist on the WebIn this paper, we proposed a method for a multi-tasking framework for real time task-based real-time image classification and summarization. The method proposes an efficient implementation using an iterative algorithm which uses the classification results to learn the underlying machine learning model and to predict the target image classification problem. This algorithm is very efficient for the task of image classification. The proposed algorithm is implemented using a generative model that encodes the image classification output and the model which can be trained locally to optimize classification. The proposed approach can be used as an in depth training for an automatic classification algorithm.