A Study of the Transfer Learning of RNNs from User Experiment and Log Data – Machine learning has shown promising results in many practical applications. However, machine learning typically requires the prediction of the outcomes on the data. In this study, we propose an end-to-end deep learning pipeline that can predict outcomes from user interaction with a machine learning classifier. On the first hand, we present a novel end-to-end pipeline for the purpose of learning neural networks from data. We show that the prediction of outcomes of users with machine learning classifiers is significantly more accurate than other prediction baselines.
The task of detecting an object is often one of identifying from the data that its boundaries are a function of its size, shape, and depth. The task is then posed as the detection of the object within the space of a set of objects and their respective shape. In this paper we develop the first algorithm for predicting the shape and depth of an object. Using the proposed approach, we build an object detector and train a deep learning library to predict its shape and depth. After training a deep neural network, we apply the CNN-STM framework to detect this object. The algorithm was applied to a toy object, and the results show good prediction performance.
Learning how to model networks
Deep Learning for Real-Time Financial Transaction Graphs with Confounding Effects of Connectomics
A Study of the Transfer Learning of RNNs from User Experiment and Log Data
Recurrent Neural Networks for Activity Recognition in Video Sequences
Spectral Clustering using Fisher Eigenvector as an Altern to k-nearest neighborsThe task of detecting an object is often one of identifying from the data that its boundaries are a function of its size, shape, and depth. The task is then posed as the detection of the object within the space of a set of objects and their respective shape. In this paper we develop the first algorithm for predicting the shape and depth of an object. Using the proposed approach, we build an object detector and train a deep learning library to predict its shape and depth. After training a deep neural network, we apply the CNN-STM framework to detect this object. The algorithm was applied to a toy object, and the results show good prediction performance.