Generating Semantic Representations using Greedy Methods – This paper presents an end-to-end approach for the semantic parsing of human language using machine learning based tools. The proposed approach is composed of two steps: (i) it learns semantic relationships by learning the representations learned by the human experts and (ii) it provides a natural way for semantic parsers to interact with the human experts. The proposed end-to-end approach is evaluated and compared with a previous state-of-the-art baseline based on Semantic Pattern Recognition (SPR) tasks. The results show that our approach outperforms the other baseline models compared to human experts without sacrificing performance by a large degree.

Traditional face detectors mainly rely on hand-written or hand-drawn sketches for detecting facial expressions. However, human models usually are not fully developed yet, so they may not be able to be used for facial expressions on a large scale. Here, we propose a non-stationary face detector based on deep Convolutional Networks (CNNs) for face detection with the goal of fully integrating them. Since CNNs allow us to model faces in images, our network aims to extract features from image images by maximizing the CNN’s ability to capture facial features for each pixel. We propose Deep-CNNs that can learn a non-stationary model that captures more detail than the one that does capture any single pixel of image. To show that our network achieves better accuracy than CNNs, we have used an image segmentation and face recognition model under various conditions. To the best of our knowledge, this is the first time we have used a CNN for face detection under such conditions. In a similar way, we also show that human model can be used to model human behavior under different conditions.

A Survey on Machine Learning with Uncertainty

On the computation of distance between two linear discriminant models

Generating Semantic Representations using Greedy Methods

Using Generalized Cross-Domain-Universal Representations for Topic Modeling

LSTM with Multi-dimensional Generative Adversarial Networks for Facial Action Unit RecognitionTraditional face detectors mainly rely on hand-written or hand-drawn sketches for detecting facial expressions. However, human models usually are not fully developed yet, so they may not be able to be used for facial expressions on a large scale. Here, we propose a non-stationary face detector based on deep Convolutional Networks (CNNs) for face detection with the goal of fully integrating them. Since CNNs allow us to model faces in images, our network aims to extract features from image images by maximizing the CNN’s ability to capture facial features for each pixel. We propose Deep-CNNs that can learn a non-stationary model that captures more detail than the one that does capture any single pixel of image. To show that our network achieves better accuracy than CNNs, we have used an image segmentation and face recognition model under various conditions. To the best of our knowledge, this is the first time we have used a CNN for face detection under such conditions. In a similar way, we also show that human model can be used to model human behavior under different conditions.