Snorkel: Efficient Strict Relaxations for Deep Neural Networks


Snorkel: Efficient Strict Relaxations for Deep Neural Networks – We present a deep learning approach for modeling a real-valued function in the form of a Bayesian network. We propose a novel hierarchical Bayesian network (HBN) for this task and show that it can be used efficiently. We first build deep architectures for neural networks and then propose a new hierarchical Bayesian network (HBN) based on a novel model of the function. We show that it can be used efficiently in both supervised and unsupervised settings, demonstrating that this approach can be used for the task at hand.

The success of deep learning is due to its ability to predict large number of points (and thus learning the entire model). We propose a novel Deep Convolutional Neural Network model that is able to learn the feature vectors of the underlying deep learning network, while retaining the ability to capture small number of point clouds. We provide a novel deep learning model for complex scene object classification and object segmentation, leveraging its ability to learn feature vectors, both at multiple scales. To this end, we train Deep Neural Network (DNN) models by using sparse models, which is an important technique in a wide range of practical applications. The proposed Deep Convolutional Network (DCNN) captures many important information within the convolutional layers, whereas the sparse model captures the whole scene at multiple scales and allows to scale to the larger number of points. We experimentally demonstrate that our proposed DCNN model achieves faster classification accuracy compared to state-of-the-art deep learning methods, in a large number of realistic scenes with a large number of hidden objects and many other objects.

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Snorkel: Efficient Strict Relaxations for Deep Neural Networks

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  • Robust Particle Induced Superpixel Classifier via the Hybrid Stochastic Graphical Model and Bayesian Model

    On the Performance of Deep Convolutional Neural Networks in Semi-Supervised Learning of Point CloudsThe success of deep learning is due to its ability to predict large number of points (and thus learning the entire model). We propose a novel Deep Convolutional Neural Network model that is able to learn the feature vectors of the underlying deep learning network, while retaining the ability to capture small number of point clouds. We provide a novel deep learning model for complex scene object classification and object segmentation, leveraging its ability to learn feature vectors, both at multiple scales. To this end, we train Deep Neural Network (DNN) models by using sparse models, which is an important technique in a wide range of practical applications. The proposed Deep Convolutional Network (DCNN) captures many important information within the convolutional layers, whereas the sparse model captures the whole scene at multiple scales and allows to scale to the larger number of points. We experimentally demonstrate that our proposed DCNN model achieves faster classification accuracy compared to state-of-the-art deep learning methods, in a large number of realistic scenes with a large number of hidden objects and many other objects.


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