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Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Deep neural network approximation to inverse conductivity problems for elliptic equations
椭圆方程 反电导率 深度神经网络
2023/4/14
The focus of this talk is on the numerical methods used to identify parameters in partial differential equations. Typically, an optimization approach is used to solve this class of inverse problems, w...
Experimental Evaluation of Deep Neural Network Resistance Against Fault Injection Attacks
fault attack neural network deep learning
2019/5/13
Deep learning is becoming a basis of decision making systems in many application domains, such as autonomous vehicles, health systems, etc., where the risk of misclassification can lead to serious con...
Deep Neural Network Attribution Methods for Leakage Analysis and Symmetric Key Recovery
Side-Channel Attacks Deep Learning Machine Learning
2019/2/26
Deep Neural Networks (DNNs) have recently received significant attention in the side-channel community due to their state-of-the-art performance in security testing of embedded systems. However, resea...
XONN: XNOR-based Oblivious Deep Neural Network Inference
Privacy-Preserving Machine Learning Deep Learning Oblivious Inference
2019/2/25
Advancements in deep learning enable cloud servers to provide inference-as-a-service for clients. In this scenario, clients send their raw data to the server to run the deep learning model and send ba...
COMPARATIVE STUDY ON DEEP NEURAL NETWORK MODELS FOR CROP CLASSIFICATION USING TIME SERIES POLSAR AND OPTICAL DATA
Deep neural networks CNNs LSTMs ConvLSTMs Crop classification
2019/2/28
Crop classification is an important task in many crop monitoring applications. Satellite remote sensing has provided easy, reliable, and fast approaches to crop classification task. In this study, a c...
STUDY ON THE CLASSIFICATION OF GAOFEN-3 POLARIMETRIC SAR IMAGES USING DEEP NEURAL NETWORK
SAR Classification Deep Learning GoogLeNet Landcover Gaofen-3 satellite
2018/5/16
Polarimetric Synthetic Aperture Radar（POLSAR） imaging principle determines that the image quality will be affected by speckle noise. So the recognition accuracy of traditional image classification met...
Neural Networks (NN) are today increasingly used in Machine Learning where they have become deeper and deeper to accurately model or classify high-level abstractions of data. Their development however...
Extracting Deep Neural Network Bottleneck Features using Low-Rank Matrix Factorization
DNN Bottleneck features
2014/11/27
In this paper, we investigate the use of deep neural networks (DNNs) to generate a stacked bottleneck (SBN) feature representation for low-resource speech recognition. We examine different SBN extract...
Extracting Deep Neural Network Bottleneck Features Using Low-Rank Matrix Factorization
DNN Bottleneck features
2015/3/9
Extracting Deep Neural Network Bottleneck Features Using Low-Rank Matrix Factorization.