[PDF] Top 20 DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks
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DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks
... proposes DeepMarks, a novel end-to-end framework for systematic fingerprinting in the context of Deep Learning ...owner. DeepMarks introduces the first fingerprinting methodology ... See full document
12
A hybrid framework combining background subtraction and deep neural networks for rapid person detection
... Typically, real-world videos are difficult to deal with due to external factors such as noise, shadows, poor illumination, low resolution, etc. Most object detection tech- niques do not account for all these problems and ... See full document
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Application of deep neural networks for security analysis of digital infrastructure componentsa
... enterprise with a perspective of being associated into a global industrial network of goods and services. The integration of a great number of fields of activity with information and network technologies has brought ... See full document
10
Deep Machine Learning In Neural Networks
... In deep learning technique the compression and efficiency acts as two ...net framework, in this framework consider 91% of the computation and accounts for 4% ...propagations, deep ... See full document
8
Joint Multitask Learning for Community Question Answering Using Task Specific Embeddings
... a framework for multitask learning of two community Question Answering problems: question-question relatedness and an- swer ...step framework based on deep neural networks and ... See full document
12
Unified Framework For Deep Learning Based Text Classification
... convolution networks also perform at par with other conventional ...artificial neural networks (ANN), k- nearest neighbor (KNN), naive Bayes classifier, decision trees, random forests, support vector ... See full document
5
Completeness Problem of the Deep Neural Networks
... After Hinton’s initial attempt of training one layer at a time, Deep Neural Networks train all layers together. Examples include TensorFlow [6], Torch [7], and Theano [8]. Google’s TensorFlow is an ... See full document
13
Deep Learning Based Crime Investigation Framework
... a framework for the entire Indian country and we want to have to generic crime matching and prediction capabilities, we create our own defined categories, in-line with the IPC codes, to classify all the ...data. ... See full document
5
Deep Belief Networks Using Convolution Neural Networks Algorithm
... convolutional neural networks. Convolutional neural networks have local receptive field architectures: each hidden unit can only connect to a small region of the ... See full document
8
Deep Neural Networks for Recommender Systems
... Ranking framework for generating ...Convolutional Neural Networks without integration with traditional systems [13] for hashtag recommendations show improvement in results as compared to existing ... See full document
5
CTR Prediction with Deep Neural Networks
... difference between supervised and unsupervised learning. The process involves using experience for gaining expertise. The experience or training contains important information that is missing in the unseen examples where ... See full document
9
Modeling Interestingness with Deep Neural Networks
... a deep semantic simi- larity model (DSSM), a special type of deep neural networks designed for text analysis, for recommending target docu- ments to be of interest to a user based on a source ... See full document
12
Review of Deep Neural Network Based on Auto encoder
... Convolutional Neural Network Based on Auto-Encoder The convolutional neural network has an efficient generalization ability for image data, but the first few layers cannot be effectively ...traditional ... See full document
8
Computational methods for predicting functions at the mRNA isoform level
... The model was validated using single mRNA isoform gene pairs, that was referred to as the “gold standard dataset”, using cross-validation. The approach was shown to be accurate when the Area Under the Receiver Operating ... See full document
49
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
Sensitivity Analysis of Deep Neural Networks
... Deep neural networks (DNNs) have exhibited impressive power in image classification and outperformed human de- tection in the ImageNet challenge (Russakovsky et ... See full document
8
Resiliency in Deep Convolutional Neural Networks
... The resilient propagation (Rprop) has been very popular for FFDN training [28]. FFDN refers to feed forward neural network where connections between the neurons do not form a loop. Combining dropout with Rprop ... See full document
109
A general purpose intelligent surveillance system for mobile devices using deep learning
... GoogLeNet [5] was Google’s submission to the ImageNet competition in 2014, which also won the competition with an impressive error rate of 6.65%. GoogLeNet is a convolutional type network that uses an advanced ... See full document
8
Blind Navigation System using Artificial Intelligence
... Logits Layer, the final layer of our neural network is the logits layer, which will return the raw values for our predictions. The logit model is a regression model where the dependent variable (DV) is ... See full document
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