A Unified Approach to Evaluating the Fitness of Classifiers – A large body of research shows how to optimize training and testing of classification models. The aim of this work is to improve the performance of state-of-the-art classification models. Two approaches were proposed for the purpose of the research: one that uses a reinforcement learning-based model, and another one that uses a Bayesian model in a two-dimensional space. In the second approach, we are interested in learning and training a Bayesian classifier using multiple-parameter Bayesian network training. Our objectives are twofold: (1) To reduce the computational time while training a learning model, and (2) To learn a Bayesian model with a very large weight. We present a method for this purpose in the framework of two-parameter Bayesian network training. We also propose a method to learn Bayes’ density-weighted representation of the Bayes’ model.
In this paper, we present a new technique for extracting 3D 3D shape from the 3D scene from a single image. We use a convolutional neural network to learn a sequence-to-sequence model for the 3D scene and train the convolutional neural network with such loss functions as 2D and 3D convolutional activations (3D+3D) as inputs. The proposed method allows us to model a 3D scene with complex 3D shape parameters and learn a sequence-to-sequence model in order to accurately predict the 3D shape from the input images. The sequence-to-sequence model is trained using the convolutional neural network in a learning and prediction network. In addition, two complementary loss functions of 2D and 3D feature (DME, DME-DME and DME+DME) as input are also used as discriminative loss functions to predict the 3D shape from the input images. The proposed model is the first to achieve promising performance on the challenging COCO dataset.
Bayesian Optimization: Estimation, Projections, and the Non-Gaussian Bloc
Image processing from multiple focus point chromatic images
A Unified Approach to Evaluating the Fitness of Classifiers
Determining Point Process with Convolutional Kernel Networks Using the Dropout Method
A New Biometric Approach for Retinal Vessel SegmentationIn this paper, we present a new technique for extracting 3D 3D shape from the 3D scene from a single image. We use a convolutional neural network to learn a sequence-to-sequence model for the 3D scene and train the convolutional neural network with such loss functions as 2D and 3D convolutional activations (3D+3D) as inputs. The proposed method allows us to model a 3D scene with complex 3D shape parameters and learn a sequence-to-sequence model in order to accurately predict the 3D shape from the input images. The sequence-to-sequence model is trained using the convolutional neural network in a learning and prediction network. In addition, two complementary loss functions of 2D and 3D feature (DME, DME-DME and DME+DME) as input are also used as discriminative loss functions to predict the 3D shape from the input images. The proposed model is the first to achieve promising performance on the challenging COCO dataset.