Recovering Questionable Clause Representations from Question-Answer Data – Answer Set Programming (ASP) is a general pattern language that has attracted tremendous attention in the recent years and is widely used to solve many large-scale scientific problems. In this paper, we present a new approach to the problem of ASP on Answer Set Programming that aims at leveraging the capabilities of the Answer Set Language, making it easier to learn it, and making the task of ASP easier. To this end, we take an ASP-like approach to answer set programming. We provide an ASP-like language with the power of Answer Set Programming with some new ASP-like tools for answer set programming. We provide an ASP-like approach in terms of using machine learning techniques to learn ASP-like languages. We discuss possible ASP-like tools in the framework of Answer Set Programming.
The Convolutional neural networks (CNN) are widely used for face recognition and pose estimation from video videos. The CNNs have a wide range of discriminant analysis capabilities and are able to accurately extract facial facial expressions from videos. CNNs have also achieved competitive performance in many tasks: semantic segmentation, object detection, object modeling, and facial pose estimation, which were considered in the literature. We propose a simple and effective framework for extracting facial expressions from videos (to the best of our knowledge) that achieves promising performance with the best of the three recognition rates by the authors. We also present some preliminary results on image retrieval tasks, as well as a recent work on action recognition. Our method was well trained on 486,000 videos of different domains (cameras) and achieved competitive success rates on the task of action recognition.
Multi-Instance Dictionary Learning in the Matrix Space with Applications to Video Classification
Recovering Questionable Clause Representations from Question-Answer Data
Dynamics from Motion in Images
Recurrent Convolutional Neural Network for Action DetectionThe Convolutional neural networks (CNN) are widely used for face recognition and pose estimation from video videos. The CNNs have a wide range of discriminant analysis capabilities and are able to accurately extract facial facial expressions from videos. CNNs have also achieved competitive performance in many tasks: semantic segmentation, object detection, object modeling, and facial pose estimation, which were considered in the literature. We propose a simple and effective framework for extracting facial expressions from videos (to the best of our knowledge) that achieves promising performance with the best of the three recognition rates by the authors. We also present some preliminary results on image retrieval tasks, as well as a recent work on action recognition. Our method was well trained on 486,000 videos of different domains (cameras) and achieved competitive success rates on the task of action recognition.