Deep Semantic Ranking over the Manifold of Pedestrians for Unsupervised Image Segmentation – We present a new approach to extracting semantic representation of images in a Bayesian network with a large number of images. This approach, termed as a cross-covariant network (ICNN), is a fast and flexible method for image segmentation that has been compared to previous approaches. A thorough evaluation of our ICNN method on several benchmark datasets shows that our ICNN outperforms the previous ones by a significant margin and is a good candidate for future large scale applications.
The main objective of this paper is to build a new framework for efficient and scalable prediction. First, a set of algorithms is trained jointly with the stochastic gradient method. Then, a stochastic gradient algorithm is proposed based on a deterministic variational model with a Bayes family of random variables. The posterior distribution of the stochastic gradient is used for inference and the random variable is estimated using a polynomial-time Monte Carlo approach. The proposed method is demonstrated with the MNIST, MNIST-2K and CIFAR-10 data sets.
Understanding a learned expert system: design, implement and test
Semi-Supervised Deep Learning for Speech Recognition with Probabilistic Decision Trees
Deep Semantic Ranking over the Manifold of Pedestrians for Unsupervised Image Segmentation
Towards A Foundation of Comprehensive Intelligent Agents for Smart Cities
Robust Decomposition Based on Robust Compressive BoundsThe main objective of this paper is to build a new framework for efficient and scalable prediction. First, a set of algorithms is trained jointly with the stochastic gradient method. Then, a stochastic gradient algorithm is proposed based on a deterministic variational model with a Bayes family of random variables. The posterior distribution of the stochastic gradient is used for inference and the random variable is estimated using a polynomial-time Monte Carlo approach. The proposed method is demonstrated with the MNIST, MNIST-2K and CIFAR-10 data sets.