[PDF] Top 20 Phone recognition with hierarchical convolutional deep maxout networks
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Phone recognition with hierarchical convolutional deep maxout networks
... shallow networks, which—just as DNNs—were trained on a block of consecutive input ...this hierarchical construct as one joint model, and he also explained why the compound structure can be interpreted as a ... See full document
13
Deep Kernel based Convolutional Neural Networks for Image Recognition
... NEURAL NETWORKS FOR VISUAL OBJECT ...The networks to benchmark datasets are applied for digit recognition (MNIST), 3D object recognition (NORB), and natural images ...a recognition test ... See full document
7
Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE
... face recognition or facial expression ...of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature ... See full document
5
Deep Learning as a Frontier of Machine Learning: A Review
... the hierarchical exploratory ...the deep learning methods avoid feature engineering in supervised learning ...data, deep learning algorithms can be applied to such kind of ...The deep belief ... See full document
9
Combination of Multiple Acoustic Models with Multi-scale Features for Myanmar Speech Recognition
... using deep convolutional neural networks (CNNs). Deep CNNs have achieved a great success in acoustic modelling for automatic speech recognition due to its ability of reducing spectral ... See full document
10
Deep Sensing: Inertia and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks
... the hierarchical structure of high-level activities because they contain more semantic and context information ...using Deep Neural Networks have focused on using data from video cameras [18] or ... See full document
29
Dense ResNet based Human Action Recognition using Novel Trajectory Maps on 3D Skeletal Data
... of Deep Convolutional Neural Networks ...the deep learning networks. The Residual Networks (ResNets) are proposed to use [1] to get higher levels of performance, to learn picture ... See full document
10
A Recent Trend in Individual Counting Approach Using Deep Network
... By producing the information of high-density, the utmost of research concentrated on an approximation of a density map to obtain the total number of the individuals [30]. These approaches were totally depending on the ... See full document
8
Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone class feature
... with phone-class ...“which phone-class the cur- rent speech frame belongs to” should be helpful for DAE to recover the clean speech from reverberant ...the phone-class fea- tures: soft and hard ... See full document
13
Deep learning model for detection of pain intensity from facial expression
... tended to focus on estimating whether the subject is in pain or not, and thus, conduct pain intensity estima- tion as a classification problem by using deep learning. While the effectiveness of these models has ... See full document
7
Deep Convolution Neural Networks for Automatic Eyeglasses Removal
... The results positively indicate that eyeglasses removal DCNN performance may be further boosted using a larger training set, but the effect of big data is not as impressive as that shown in high-level vision problems. ... See full document
8
Wildlife surveillance using deep learning methods
... our recognition framework is unable to recognize more than one animal category in the same image, nor can it recognize how many anima s are present Adaptations to enab e these features would allow automatic ... See full document
15
MODEL OF CYBERSECURITY MEANS FINANCING WITH THE PROCEDURE OF ADDITIONAL DATA OBTAINING BY THE PROTECTION SIDE
... Convolution Neural Network (CNN) is a kind of deep artificial neural network that is commonly used in analyzing images. It learns features that are related spatially by treating an image as a volume. CNN has some ... See full document
9
Hierarchical Convolutional Attention Networks for Text Classification
... RNN-based approaches for text processing can in- herently account for word order when extracting features. However, feedforward and convolution- based approaches such as our implementation of convolutional ... See full document
13
Classification of Age and Gender using Deep Learning
... Deep learning is a class of machine learning algorithms that cause a precipitation of voluminous layers of nonlinear processing units for feature extraction and change. Each succeeding layer utilizes the yield ... See full document
6
A Segmentation Model for Extracting Farmland and Woodland from Remote Sensing Image
... Fully Convolutional Networks (FCN) is a deep learning network for image segmentation.. 67.[r] ... See full document
18
Deep convolutional neural networks capabilities for
... for the detection of MCs based on the use of deep convolutional neural networks (DCNNs).. We.[r] ... See full document
26
Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics
... for the detection of MCs based on the use of deep convolutional neural networks (DCNNs).. As a 21.[r] ... See full document
25
Individual Minke Whale Recognition Using Deep Learning Convolutional Neural Networks
... VGG16 convolutional layers, which were loaded with the Imagenet-trained VGG16 weights available in Keras ...their convolutional equivalents as per the FCN-8s ...non-VGG16 convolutional layers were ... See full document
12
Opisthenar : hand poses and finger tapping recognition by observing back of hand using embedded wrist camera
... the data for 600 frames. Therefore, the labelled data contains a mixture of no gesture, down gesture and up gesture, each with different magnitude and velocity. A better approach might be to synchronize and only record ... See full document
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