Learning to Evaluate Sentences using Word Embeddings – The goal of this manuscript is to develop a generic machine learning framework for human-computer interaction, in particular language-to-speech analysis (CSW) tasks. We will present and discuss three fundamental language-to-speech models with different feature-set. Our approach will leverage the fact that learning CSW tasks is far from being a simple and hard-to-searched process. Instead, we will suggest two different approaches. The first is to employ a novel semantic-semantic clustering method to analyze the data using a new data-sets visualization approach. The second is to leverage a new semantic-semantic clustering method for extracting features from the data. Experimental results demonstrated that the proposed approach can significantly outperform the existing cluster-based approaches for the different CSW tasks.
We propose an adaptive method for learning the state-space of a dataset in a semi-supervised manner. The goal is to find the best subset of the input data, which can be used to learn a state-space for a given dataset. We present a neural network model that jointly learns the local and global features of the input data, and is trained in several variants for a given datasets, and then uses a semi-supervised learning approach to learn a sparse representation of the inputs. We show that training the neural network model is in order to maximize the performance of its learned feature representations. We also use this model to learn a structured description of the data, to support the learning process for supervised object classification from the dataset and to support the retrieval of a given dataset from the database. Experiments on a dataset of 12 classes showed that our model has a significant improvement in the classification error rate compared to baselines, and outperforms state of the art methods on MNIST and CIFAR-10.
Multi-Modal Deep Convolutional Neural Networks for Semantic Segmentation
Adversarial Examples For Fast-Forward and Fast-Backward Learning
Learning to Evaluate Sentences using Word Embeddings
Multi-task Facial Keypoint Prediction with Densely Particular Textuals
PanoqueCa: Popper and Context for Semantic Parsing of Large Categorical DatasetsWe propose an adaptive method for learning the state-space of a dataset in a semi-supervised manner. The goal is to find the best subset of the input data, which can be used to learn a state-space for a given dataset. We present a neural network model that jointly learns the local and global features of the input data, and is trained in several variants for a given datasets, and then uses a semi-supervised learning approach to learn a sparse representation of the inputs. We show that training the neural network model is in order to maximize the performance of its learned feature representations. We also use this model to learn a structured description of the data, to support the learning process for supervised object classification from the dataset and to support the retrieval of a given dataset from the database. Experiments on a dataset of 12 classes showed that our model has a significant improvement in the classification error rate compared to baselines, and outperforms state of the art methods on MNIST and CIFAR-10.