Morphon: a collection of morphological and semantic words


Morphon: a collection of morphological and semantic words – We describe a new model-based generative adversarial network (GAN) for semantic object classification. We propose a novel method which uses a convolutional neural network to learn a new set of discriminative representations for the domain model which can be used to learn models for a variety of different semantic object categories. As we study the task of classification of semantic objects, we propose an unsupervised CNN to learn discriminative representations of semantic words. We validate our work by analyzing various benchmarks including MNIST and the state of the art state of the art SPMVM for semantic object classification. After training a discriminative learning network we are able to classify object classes from the input semantic sentence. The discriminative representations from the CNN have also been used to predict the object class from the learned representations. We test our method and compare it to other existing state of the art supervised object classification methods on the MNIST and SPMVM datasets. The classification accuracies are higher for the MNIST and SPMVM than for other state of the art supervised classification algorithms but only a few metrics are evaluated to show the performance.

In this paper, we propose a new fully convolutional neural network (FCNN) to tackle the 3D object recognition problem. We propose Convolutional Neural Network (CNN) for grasping 3D objects from videos. The CNN is trained end to end, with the aim of learning object detection and trajectory based object classification, without using any hand-crafted convolutional features. Compared to existing CNN models with a very small number of parameters, our CNN has a few parameters which are more discriminative to improve object detection. We show that our CNN is not only able to reliably classify high quality object instances without any hand-crafted object features. This is important because CNN can be used for improving object category accuracy if the 2D object recognition process is used. In addition to CNN, our CNN is also able to accurately classify objects which are very dense objects. Our CNN is implemented using an interactive 3D object prediction platform which demonstrates our accuracy on the challenging task of 2D objects classification on a 3D MNIST dataset.

Inference from Sets with and Without Inputs: Unsupervised Topic Models and Bayesian Queries

Bayesian Nonparametric Modeling

Morphon: a collection of morphological and semantic words

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  • Towards CNN-based Image Retrieval with Multi-View Fusion

    Deep Learning with a Unified Deep Convolutional Network for Video ClassificationIn this paper, we propose a new fully convolutional neural network (FCNN) to tackle the 3D object recognition problem. We propose Convolutional Neural Network (CNN) for grasping 3D objects from videos. The CNN is trained end to end, with the aim of learning object detection and trajectory based object classification, without using any hand-crafted convolutional features. Compared to existing CNN models with a very small number of parameters, our CNN has a few parameters which are more discriminative to improve object detection. We show that our CNN is not only able to reliably classify high quality object instances without any hand-crafted object features. This is important because CNN can be used for improving object category accuracy if the 2D object recognition process is used. In addition to CNN, our CNN is also able to accurately classify objects which are very dense objects. Our CNN is implemented using an interactive 3D object prediction platform which demonstrates our accuracy on the challenging task of 2D objects classification on a 3D MNIST dataset.


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