On the Impact of Data Streams on the Training of Neural Networks – The problem of finding a good predictor for a given data is one of the main obstacles to learning. The main problem in this work is to find the optimum between the expected prediction and actual distribution of the data. The best predictor is assumed to be the best predictive performance for the given data; however, this prediction can be a poor predictor of actual distribution because of the large amount of covariates, but also because of the variable dimension of the regression problems and the lack of a good predictor. The main issue in this work is to make use of the existing variational methods. We first show how to use the variational framework to develop an appropriate decision function, that is a Bayesian-constrained algorithm that can estimate the actual distribution of the covariates. The estimation is made by computing a global optimum for a Gaussian distribution and then predicting the maximum likelihood. We propose two algorithms, one using Bayes’ method and the other one using the variational approach that assumes the expected distribution of data is the true distribution. We also establish that both approaches have advantages over each other in terms of their performance.
In this paper, we describe a new approach for multi-task semantic segmentation with recurrent neural network. Our approach combines two tasks: semantic segmentation by recurrent neural networks and object segmentation from a video or audio source. In an end-to-end training framework, we develop an end-to-end policy generation framework, and present the first system for multi-task semantic segmentation with recurrent neural network (RNN) and CNNs. We have developed a multi-task semantic segmentation model with recurrent-CNN network from scratch and train it on different state-of-the-art Residual Reinforcement Learning (ResRRL) models. Our experiments show that our approach is highly accurate and can achieve state-of-the-art performance.
Boosting for Deep Supervised Learning
Exploiting the Sparsity of Deep Neural Networks for Predictive-Advection Mining
On the Impact of Data Streams on the Training of Neural Networks
Online Model Interpretability in Machine Learning Applications
Recovering language from scratch using probabilistic word embeddings for unconstrained and unsupervised learningIn this paper, we describe a new approach for multi-task semantic segmentation with recurrent neural network. Our approach combines two tasks: semantic segmentation by recurrent neural networks and object segmentation from a video or audio source. In an end-to-end training framework, we develop an end-to-end policy generation framework, and present the first system for multi-task semantic segmentation with recurrent neural network (RNN) and CNNs. We have developed a multi-task semantic segmentation model with recurrent-CNN network from scratch and train it on different state-of-the-art Residual Reinforcement Learning (ResRRL) models. Our experiments show that our approach is highly accurate and can achieve state-of-the-art performance.