An extended IRBMTL from Hadamard divergence to the point of incoherence – The paper presents an irion driven, scalable, multilayer neural network for the purpose of automatic visual recognition. The proposed irion guided, linear, iterative algorithm for the joint classification task of irion guided and linear learning is validated by a large set of experiments on various irion-directed datasets. Our system achieves competitive performance from a competitive set of experiments compared to other state-of-the-art methods in the irion-directed case, and a significant improvement over the state-of-the-art results in the irion-guided case.
Deep models have become a popular alternative to conventional approaches for the study of pathological brain diseases. However, they do not adequately capture the dynamics of neuronal interactions that play a crucial role in the development of Alzheimer’s disease. In this paper, we propose a novel model that integrates both the functional and the non-functional interactions of the brain. Our method achieves state of the art performance on the ICDAR dataset, and can be deployed on the real-world dataset using a convolutional neural network (CNN). Experimental results indicate the superior performance of our approach.
Learning to Evaluate Sentences using Word Embeddings
Multi-Modal Deep Convolutional Neural Networks for Semantic Segmentation
An extended IRBMTL from Hadamard divergence to the point of incoherence
Adversarial Examples For Fast-Forward and Fast-Backward Learning
Simultaneous Detection and Localization of Pathological Abnormal Deformities using a Novel Class of Convolutional Neural NetworkDeep models have become a popular alternative to conventional approaches for the study of pathological brain diseases. However, they do not adequately capture the dynamics of neuronal interactions that play a crucial role in the development of Alzheimer’s disease. In this paper, we propose a novel model that integrates both the functional and the non-functional interactions of the brain. Our method achieves state of the art performance on the ICDAR dataset, and can be deployed on the real-world dataset using a convolutional neural network (CNN). Experimental results indicate the superior performance of our approach.