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.
In this work, we propose an efficient and robust method for clustering large-scale objects in visual datasets. Unlike other methods for clustering large-scale objects, the proposed algorithm requires a novel hierarchical embedding structure which reduces the number of steps required to learn to search a large-scale object within an image. We evaluate the proposed model on a simulated dataset and demonstrate its superior state-of-the-art performance on the challenging MNIST dataset with a significantly more challenging object.
Towards a Theory of Neural Style Transfer
Multi-label Visual Place Matching
Learning to Evaluate Sentences using Word Embeddings
A Novel Face Alignment Based on Local Contrast and Local Hue
Robust k-nearest neighbor clustering with a hidden-chevelleIn this work, we propose an efficient and robust method for clustering large-scale objects in visual datasets. Unlike other methods for clustering large-scale objects, the proposed algorithm requires a novel hierarchical embedding structure which reduces the number of steps required to learn to search a large-scale object within an image. We evaluate the proposed model on a simulated dataset and demonstrate its superior state-of-the-art performance on the challenging MNIST dataset with a significantly more challenging object.