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Found 21 papers, 9 papers with code
CD&S Dataset: Handheld Imagery Dataset Acquired Under Field Conditions for Corn Disease Identification and Severity Estimation
Accurate disease identification and its severity estimation is an important consideration for disease management.
Towards End-to-End Deep Learning for Autonomous Racing: On Data Collection and a Unified Architecture for Steering and Throttle Prediction
Deep Neural Networks (DNNs) which are trained end-to-end have been successfully applied to solve complex problems that we have not been able to solve in past decades.
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression
To reduce the training time of the defense for a small trade-off in performance, we propose the hidden layer defense, which involves feeding the output of the encoder of a denoising autoencoder into the network.
Knowledge Distillation For Wireless Edge Learning
In this paper, we propose a framework for predicting frame errors in the collaborative spectrally congested wireless environments of the DARPA Spectrum Collaboration Challenge (SC2) via a recently collected dataset.
Gradient-based Adversarial Deep Modulation Classification with Data-driven Subsampling
Automatic modulation classification can be a core component for intelligent spectrally efficient wireless communication networks, and deep learning techniques have recently been shown to deliver superior performance to conventional model-based strategies, particularly when distinguishing between a large number of modulation types.
A Provably Convergent Information Bottleneck Solution via ADMM
Conventionally, it resorts to characterizing the information plane, that is, plotting $I(Y;Z)$ versus $I(X;Z)$ for all solutions obtained from different initial points.
Data-driven Analysis of Turbulent Flame Images
Pockets or islands of unburned material are features of turbulent flames during these events.
Deep Learning for DOA Estimation in MIMO Radar Systems via Emulation of Large Antenna Arrays
We further analyze the best choice for the training SNR as a function of the test SNR, and observe dramatic changes in the behavior of this function for different angle ranges.
Ensemble Wrapper Subsampling for Deep Modulation Classification
Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms that rely on the output samples.
Deep Learning for Frame Error Prediction using a DARPA Spectrum Collaboration Challenge (SC2) Dataset
We demonstrate a first example for employing deep learning in predicting frame errors for a Collaborative Intelligent Radio Network (CIRN) using a dataset collected during participation in the final scrimmages of the DARPA SC2 challenge.
Non-Intrusive Detection of Adversarial Deep Learning Attacks via Observer Networks
In this letter, we propose a novel method to detect adversarial inputs, by augmenting the main classification network with multiple binary detectors (observer networks) which take inputs from the hidden layers of the original network (convolutional kernel outputs) and classify the input as clean or adversarial.
Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks
Gradient-based adversarial attacks on neural networks can be crafted in a variety of ways by varying either how the attack algorithm relies on the gradient, the network architecture used for crafting the attack, or both.
Efficient Training of Deep Classifiers for Wireless Source Identification using Test SNR Estimates
We study efficient deep learning training algorithms that process received wireless signals, if a test Signal to Noise Ratio (SNR) estimate is available.
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks
The reliance on deep learning algorithms has grown significantly in recent years.
Efficient Wrapper Feature Selection using Autoencoder and Model Based Elimination
We propose a computationally efficient wrapper feature selection method - called Autoencoder and Model Based Elimination of features using Relevance and Redundancy scores (AMBER) - that uses a single ranker model along with autoencoders to perform greedy backward elimination of features.
Deep Learning for Interference Identification: Band, Training SNR, and Sample Selection
We study the problem of interference source identification, through the lens of recognizing one of 15 different channels that belong to 3 different wireless technologies: Bluetooth, Zigbee, and WiFi.
Fast Deep Learning for Automatic Modulation Classification
We then study algorithms to reduce the training time by minimizing the size of the training data set, while incurring a minimal loss in classification accuracy.
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder Approach
Machine Learning models are vulnerable to adversarial attacks that rely on perturbing the input data.
Deep Neural Network Architectures for Modulation Classification
Finally, we introduce a Convolutional Long Short-term Deep Neural Network (CLDNN [4]) to achieve an accuracy of approximately 88. 5% at high SNR.
A Sampling Theory Perspective of Graph-based Semi-supervised Learning
In this work, we reinforce this connection by viewing the problem from a graph sampling theoretic perspective, where class indicator functions are treated as bandlimited graph signals (in the eigenvector basis of the graph Laplacian) and label prediction as a bandlimited reconstruction problem.
Asymptotic Justification of Bandlimited Interpolation of Graph signals for Semi-Supervised Learning
Graph-based methods play an important role in unsupervised and semi-supervised learning tasks by taking into account the underlying geometry of the data set.
