[PDF] Top 20 Learning (from) Deep Hierarchical Structure among Features
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Learning (from) Deep Hierarchical Structure among Features
... root to each leaf node are summed to 1, the proposed ob- jective function can be proved to be convex no matter what the height of the hierarchical structure is. Moreover, when all the exponents take the ... See full document
8
Lung Disease Classification using GLCM and Deep Features from Different Deep Learning Architectures with Principal Component Analysis
... comes from studies of various authors that managed to use a deep learning approach to form a new set of deep features for various classification purposes and modalities such as ocular ... See full document
14
Classification with Costly Features Using Deep Reinforcement Learning
... We build upon work of Dulac-Arnold et al. (2011), which used Q-learning with linear regression, resulting in limited performance. We replace the linear approximation with neu- ral networks, extend the approach ... See full document
8
A Robust Visual Tracking Method through Deep Learning Features
... Learning features by convolution neural network from raw image pixels on large scale dataset has made impressive progress compared with hand-crafted features ...The deep learning ... See full document
6
Deep Learning of Audio and Language Features for Humor Prediction
... machine learning methods to predict and detect humor in ...dialogues from a very popular TV sitcom: “The Big Bang ...frame-level features, the CNN outperforms the other methods, obtaining the best ... See full document
6
Learning deep features from body and parts for person re identification in camera networks
... features. We adopt the identification model for learning body-based features as shown in Fig. 2b and also utilize the ResNet-50 [21] as the CNN model. We take the holis- tic pedestrian images as the ... See full document
8
Hierarchical level features based trainable segmentation for electron microscopy images
... ensemble learning consists of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest ...selected from ... See full document
14
AN IOT BASED FRAMEWORK FOR STUDENTS INTERACTION AND PLAGIARISM DETECTION IN PROGRAMMING ASSIGNMENTS
... using deep learning with Ontology to tackle these ...multimedia features, identifying and classifying objects and media format with Convolutional Neural Network (CNN) and Recurrent Neural Network ... See full document
17
Learning Hierarchical Translation Structure with Linguistic Annotations
... 2010). Hierarchical translation was combined with target side linguistic annotation in (Zollmann and Venu- gopal, ...suffers from inadequate constraints in the translation rules extracted, or from ... See full document
11
Using CNN and HOG Classifier to Improve Facial Expression Recognition
... recognition. Deep learning is an area in computer vision and machine learning which is promising in terms of improving ...perceptions from a number of multiple hidden ones enabling easy ... See full document
5
Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High Grade Gliomas
... using deep features extracted by VGG- 19, an advanced model with documented excellent perform- ance in image ...classification. Deep features are not limited to previously identified image ... See full document
9
A Study on Deep Learning Networks for Detecting Alzheimer’s Disease Gayathri V S 1and Deepa P L2
... terms Deep Learning provides many applications by achieving great ...conventional learning architectures, Deep learning provides better data representation of high level features ... See full document
7
Human-level Moving Object Recognition from Traffic Video
... reinforcement learning takes form of external ...network learning efficiency, and falling to local optimal. Deep neural network is a multiple neural network learning model based on deep ... See full document
14
Cell recognition based on topological sparse coding for microscopy imaging of focused ultrasound treatment
... are features that are widely used to represent shape characteristics, local structural infor- mation, and local visual saliency, ...visual features from every nuclei ...into deep ... See full document
8
Research Review and Prospect of Fault Diagnosis Method of Satellite Power System Based on Machine Learning
... The deep learning method has obvious advantages in the aspect of fault ...extract features automatically and efficiently from a lot of information, can determine the correlation between the ... See full document
7
Deep Learning via Stacked Sparse Autoencoders for Automated Voxel-Wise Brain Parcellation Based on Functional Connectivity
... of deep artificial neural network (Hochreiter, ...stemmed from the fact that as a layer of neural network eventually learned a task reasonably well, the learned features were not successfully ... See full document
86
Exploiting Structure for Scalable and Robust Deep Learning
... a hierarchical baseline without an attention model produces a straight-line rollout (column e, bottom), whereas the HPN produces a more natural movement curve (column e, ... See full document
141
Advanced Machine Learning Approach: Deep Learning
... the deep learning approach, the efficiency of image recognition and object detection has increased dramatically over the past seven ...common features, such as scene classification, object detection, ... See full document
5
Keyword Spotting in Images Using Convolutional Neural Network
... machine learning approaches. Machine vision and deep learning have been booming with a greater speed in past few years thus thus there is always a better solution or a problem every now and ...the ... See full document
5
Detecting context abusiveness using hierarchical deep learning
... ing deep learning. We have designed a hierarchi- cal deep learning model that extracts global fea- tures for long ...our hierarchical model outperforms in implicit abusive sentences of ... See full document
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