[PDF] Top 20 On the Performance of Convolutional Neural Networks for Side-channel Analysis
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On the Performance of Convolutional Neural Networks for Side-channel Analysis
... Profiled side-channel attacks are recognized as the most powerful ones since they define the worst case security assumptions. There, the attacker has access to a clone device, which can be profiled for any ... See full document
19
Make Some Noise: Unleashing the Power of Convolutional Neural Networks for Profiled Side-channel Analysis
... of neural networks which were first designed for 2- dimensional convolutions as it was inspired by the biological processes of animals’ visual cortex ...in side-channel analysis has ... See full document
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Kilroy was here: The First Step Towards Explainability of Neural Networks in Profiled Side-channel Analysis
... To test this hypothesis, we now aim to design a smaller multilayer percep- tron architecture with better performance. We conducted a grid search with 0–5 hidden layers, having 100/200/300/400/500 neurons per ... See full document
23
Efficient Convolutional Neural Networks for Diacritic Restoration
... For Vietnamese, when we re-train N´aplava et al. (2018) model on the same sample discussed in Section 4, both A-TCN and BiLSTM provide sig- nificantly better results. N´aplava et al. (2018) also use BiLSTM but with ... See full document
7
Brain Tumor Classification Using Convolutional Neural Networks
... The main goal of this research work is to design efficient automatic brain tumor classification with high accuracy, performance and low complexity. In the conventional brain tumor classification is performed by ... See full document
5
Projection Convolutional Neural Networks for 1-bit CNNs via Discrete Back Propagation
... While reducing storage requirements greatly, these BNNs generally have significant accuracy degradation, compared to those using the full-precision kernels. This is primar- ily due to the following two reasons. (1) The ... See full document
8
Spam detection in im images using convolutional neural networks
... In the context of this paper, I’m scraping sample images from the internet, and indexing them iteratively, at first, to create my desired dataset. Once the dataset was ready, then I wrote some Tensor-flow functions to ... See full document
6
An Analysis of Convolutional Neural Networks for Image Recognition
... The pooling types (pooling function) include mean-pooling, max-pooling. Mean-pooling takes the mean value of each sub-region as the result, and max-pooling takes the maximum value of each sub-region as the result. ... See full document
5
Multi-Channel Distributed Representation for Classifying Tweets by using Convolutional Neural Networks
... Table I shows the experimental results, presenting preci- sion, recall, F-measure of “relevant” class, and accuracy. In addition, after learning each model on training data, each model was evaluated through ... See full document
6
Deep Convolution Neural Networks for Automatic Eyeglasses Removal
... recognition performance. Inspired by the success of deep convolutional neural networks (DCNN) on super resolution, in this paper, a method based on deep convolutional neural ... See full document
8
Improved Study of Side-Channel Attacks Using Recurrent Neural Networks
... profiled Side-Channel Analysis (SCA) and compared the performance of several machine learning ...use convolutional neural networks (CNNs) for good ...high ... See full document
78
A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification
... Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (Kim, 2014; Kalchbrenner et ...model ... See full document
11
Applications of Convolutional Neural Networks
... Recurrent convolutional models are doubly ...a performance of 28.8% whereas the TACoS achieve a performance of ...of neural networks is used in order to train CNN on high quality ... See full document
10
Exploring Convolutional Neural Networks for Sentiment Analysis of Spanish tweets
... As future work, we also plan to study if the inclusion of external features (such as sentiment features from polarity lexicons) to the final layer (softmax layer) achieves to improve the results. Because the General ... See full document
9
Sentiment Classification Via Recurrent Convolutional Neural Networks
... Table I shows the experimental results of prediction accuracies. “All” represents the prediction of all nodes in sentences (phase level), while “root” represents the prediction of a sentence (sentence-level). In ... See full document
9
Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts
... deep convolutional neural network that exploits from character- to sentence- level information to perform sentiment analysis of short ...Sentence Convolutional Neural Network ... See full document
10
The Sockeye Neural Machine Translation Toolkit at AMTA 2018
... success, Neural Machine Translation (NMT) presents a range of new ...“production-ready” performance in both translation quality and computational ...a neural sequence-to-sequence toolkit written in ... See full document
8
Detection of Sarcasm in Text Data using Deep Convolutional Neural Networks
... Sarcasm is a well known, commonly used and well-studied topic in linguistics. In spite of being so widely used and being part of our speech, it’s inherently very challenging not only for machines but for humans also to ... See full document
10
The Application of Neural Network in Multiple Object Tracking
... video analysis and ...of neural networks in MOT, such as feature extraction, model formulation of both appearance and ...in neural networks has shown competitive superiority and drawn ... See full document
7
Semantic Segmentation on Remotely Sensed Images Using an Enhanced Global Convolutional Network with Channel Attention and Domain Specific Transfer Learning
... high performance on different benchmarks ...fully convolutional networks ...global convolutional network (GCN) as shown in Figure ...localization performance near the ... See full document
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