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Found 47 papers, 0 papers with code
Harnessing the Power of Noise: A Survey of Techniques and Applications
Noise, traditionally considered a nuisance in computational systems, is reconsidered for its unexpected and counter-intuitive benefits across a wide spectrum of domains, including nonlinear information processing, signal processing, image processing, machine learning, network science, and natural language processing.
Interpretable Data Fusion for Distributed Learning: A Representative Approach via Gradient Matching
This paper introduces a representative-based approach for distributed learning that transforms multiple raw data points into a virtual representation.
Anomaly Detection via Learning-Based Sequential Controlled Sensing
Our objective is to design a sequential selection policy that dynamically determines which processes to observe at each time with the goal to minimize the delay in making the decision and the total sensing cost.
On Distributed and Asynchronous Sampling of Gaussian Processes for Sequential Binary Hypothesis Testing
The distributed system is such that the sensors and the FC sample observations periodically, where the sampling times are not necessarily synchronous, i. e., the sampling times at different sensors and the FC may be different from each other.
On Gibbs Sampling Architecture for Labeled Random Finite Sets Multi-Object Tracking
However, only a limited discussion has been provided regarding key Gibbs sampler architecture details including the Markov chain Monte Carlo sample generation technique and early termination criteria.
Distributed Quantized Detection of Sparse Signals Under Byzantine Attacks
The proposed detectors can achieve the detection performance close to the benchmark likelihood ratio test (LRT) detector, which has perfect knowledge of the attack parameters and sparsity degree.
COMIC: An Unsupervised Change Detection Method for Heterogeneous Remote Sensing Images Based on Copula Mixtures and Cycle-Consistent Adversarial Networks
For this problem, an unsupervised change detection method has been proposed recently based on the image translation technique of Cycle-Consistent Adversarial Networks (CycleGANs), where one image is translated from its original modality to the modality of the other image so that the difference map can be obtained by performing arithmetical subtraction.
NN-Copula-CD: A Copula-Guided Interpretable Neural Network for Change Detection in Heterogeneous Remote Sensing Images
To enhance the interpretability of existing neural networks for CD, we propose a knowledge-data-driven heterogeneous CD method based on a copula-guided neural network, named NN-Copula-CD.
Human-machine Hierarchical Networks for Decision Making under Byzantine Attacks
Consequently, the proposed scheme can effectively defend against Byzantine attacks and improve the quality of human sensors' decisions so that the performance of the human-machine collaborative system is enhanced.
Loss Attitude Aware Energy Management for Signal Detection
The amount of resource consumption that maximizes the humans' subjective utility is derived to characterize the actual behavior of humans.
Human-Machine Collaboration for Smart Decision Making: Current Trends and Future Opportunities
Recently, modeling of decision making and control systems that include heterogeneous smart sensing devices (machines) as well as human agents as participants is becoming an important research area due to the wide variety of applications including autonomous driving, smart manufacturing, internet of things, national security, and healthcare.
Sequential Processing of Observations in Human Decision-Making Systems
In this work, we consider a binary hypothesis testing problem involving a group of human decision-makers.
Performance Analysis of LEO Satellite-Based IoT Networks in the Presence of Interference
The paper also presents simplified expressions for the OP under a high signal-to-noise ratio (SNR) assumption, which are utilized to optimize the system parameters for achieving a target OP.
Efficient Ordered-Transmission Based Distributed Detection under Data Falsification Attacks
In distributed detection systems, energy-efficient ordered transmission (EEOT) schemes are able to reduce the number of transmissions required to make a final decision.
Distributed Estimation in Large Scale Wireless Sensor Networks via a Two Step Group-based Approach
To achieve greater sensor transmission and estimation efficiency, we propose a two step group-based collaborative distributed estimation scheme, where in the first step, sensors form dependence driven groups such that sensors in the same group are highly dependent, while sensors from different groups are independent, and perform a copula-based maximum a posteriori probability (MAP) estimation via intragroup collaboration.
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms
In this paper, we consider nonconvex minimax optimization, which is gaining prominence in many modern machine learning applications such as GANs.
Ordered Transmission-based Detection in Distributed Networks in the Presence of Byzantines
The ordered transmission (OT) scheme reduces the number of transmissions needed in the network to make the final decision, while it maintains the same probability of error as the system without using OT scheme.
Temporal Detection of Anomalies via Actor-Critic Based Controlled Sensing
Based on the received observations, the decisionmaker first determines whether to declare that the number of anomalies has exceeded the threshold or to continue taking observations.
Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing
In this setting, we develop an anomaly detection algorithm that chooses the processes to be observed at a given time instant, decides when to stop taking observations, and declares the decision on anomalous processes.
Multi-sensor Joint Adaptive Birth Sampler for Labeled Random Finite Set Tracking
A naive construction of the multi-sensor measurement adaptive birth set distribution leads to an exponential number of newborn components in the number of sensors.
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning
Despite extensive research, for a generic non-convex FL problem, it is not clear, how to choose the WNs' and the server's update directions, the minibatch sizes, and the local update frequency, so that the WNs use the minimum number of samples and communication rounds to achieve the desired solution.
Anomaly Detection via Controlled Sensing and Deep Active Inference
In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes.
A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing
In this setting, we develop an anomaly detection algorithm that chooses the process to be observed at a given time instant, decides when to stop taking observations, and makes a decision regarding the anomalous processes.
Decentralized Federated Learning via Mutual Knowledge Transfer
In this paper, we investigate the problem of decentralized federated learning (DFL) in Internet of things (IoT) systems, where a number of IoT clients train models collectively for a common task without sharing their private training data in the absence of a central server.
Zeroth-Order Hybrid Gradient Descent: Towards A Principled Black-Box Optimization Framework
In this work, we focus on the study of stochastic zeroth-order (ZO) optimization which does not require first-order gradient information and uses only function evaluations.
Joint Collaboration and Compression Design for Distributed Sequential Estimation in a Wireless Sensor Network
We propose two versions of compression design, one centralized where the compression strategies are derived at the FC and the other decentralized, where the local sensors compute their individual compression matrices independently.
A Novel Spectrally-Efficient Uplink Hybrid-Domain NOMA System
This paper proposes a novel hybrid-domain (HD) non-orthogonal multiple access (NOMA) approach to support a larger number of uplink users than the recently proposed code-domain NOMA approach, i. e., sparse code multiple access (SCMA).
A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning
Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many signal processing and machine learning applications.
Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning
Our objective is to design a sequential sensor selection policy that dynamically determines which processes to observe at each time and when to terminate the detection algorithm.
Distributed Stochastic Non-Convex Optimization: Momentum-Based Variance Reduction
In this work, we propose a distributed algorithm for stochastic non-convex optimization.
Optimization and Control Distributed, Parallel, and Cluster Computing
Anomalous Example Detection in Deep Learning: A Survey
This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for DL based applications.
Parallel Restarted SPIDER -- Communication Efficient Distributed Nonconvex Optimization with Optimal Computation Complexity
For online problems ($n$ unknown or infinite), we achieve the optimal IFO complexity $O(\epsilon^{-3/2})$.
Prospect Theory Based Crowdsourcing for Classification in the Presence of Spammers
We consider the $M$-ary classification problem via crowdsourcing, where crowd workers respond to simple binary questions and the answers are aggregated via decision fusion.
K-medoids Clustering of Data Sequences with Composite Distributions
In particular, the error exponent is characterized when either the Kolmogrov-Smirnov distance or the maximum mean discrepancy are used as the distance metric.
Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory
Such arguments, however, fail to acknowledge that the overall decision-making system is composed of two entities: the learned model and a human who fuses together model outputs with his or her own information.
Decision Tree Design for Classification in Crowdsourcing Systems
In this paper, we present a novel sequential paradigm for classification in crowdsourcing systems.
Human-Machine Inference Networks For Smart Decision Making: Opportunities and Challenges
The emerging paradigm of Human-Machine Inference Networks (HuMaINs) combines complementary cognitive strengths of humans and machines in an intelligent manner to tackle various inference tasks and achieves higher performance than either humans or machines by themselves.
A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms
Third, we propose an efficient estimator to evaluate the space-filling properties of sample designs in arbitrary dimensions and use it to develop an optimization framework to generate high quality space-filling designs.
A Memristor-Based Optimization Framework for AI Applications
In addition to ADMM, implementation of a customized power iteration (PI) method for eigenvalue/eigenvector computation using memristor crossbars is discussed.
Robust Decentralized Learning Using ADMM with Unreliable Agents
We also provide conditions on the erroneous updates for exact convergence to the optimal solution.
Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization
Then, by exploiting the Kurdyka-{\L}ojasiewicz (\KL) property for a broad class of functions, we establish the linear and sub-linear convergence rates of the function value sequence generated by APGnc.
Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models
Influential node detection is a central research topic in social network analysis.
Towards the Design of Prospect-Theory based Human Decision Rules for Hypothesis Testing
Detection rules have traditionally been designed for rational agents that minimize the Bayes risk (average decision cost).
Multi-object Classification via Crowdsourcing with a Reject Option
We present an aggregation approach using a weighted majority voting rule, where each worker's response is assigned an optimized weight to maximize the crowd's classification performance.
Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach
This paper considers the problem of high dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration).
Consensus based Detection in the Presence of Data Falsification Attacks
This paper considers the problem of detection in distributed networks in the presence of data falsification (Byzantine) attacks.
Hybrid Maximum Likelihood Modulation Classification Using Multiple Radios
The performance of a modulation classifier is highly sensitive to channel signal-to-noise ratio (SNR).
