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Recursive inversion models for permutations
We develop a new exponential family model for permutations that can capture hierarchical structure in preferences, and that has the well known...
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Generalizing Frobenius inversion to quaternion matrices
In this paper, we derive and analyze an algorithm for inverting quaternion matrices. The algorithm is an analogue of the Frobenius algorithm for the...
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Filter-cluster attention based recursive network for low-light enhancement
The poor quality of images recorded in low-light environments affects their further applications. To improve the visibility of low-light images, we...
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Exploring conditional pixel-independent generation in GAN inversion for image processing
Image processing holds an indispensable role in various facets of our daily lives, professional undertakings, and educational pursuits, encompassing...
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Construct a Secure CNN Against Gradient Inversion Attack
Federated learning enables collaborative model training across multiple clients without sharing raw data, adhering to privacy regulations, which...
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Recursive least squares method for training and pruning convolutional neural networks
Convolutional neural networks (CNNs) have shown good performance in many practical applications. However, their high computational and storage...
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A new approach for mechanical parameter inversion analysis of roller compacted concrete dams using modified PSO and RBFNN
The mechanical parameter inversion model is an essential part of ensuring dam health; it provides a parametric basis for assessing the safe...
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Inversion of Control
Inversion of control involves reversing the usual flow of control from caller code to called code to achieve separation of concerns and loose...
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Evaluating differentially private decision tree model over model inversion attack
Machine learning techniques have been widely used and shown remarkable performance in various fields. Along with the widespread utilization of...
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Multi-scale recursive codec network with authority parameters (MRCN-AP) for RFID multi-label deblurring
The dynamic non-uniform blur caused by Radio Frequency Identification (RFID) multi-label motion seriously affects the identification and location of...
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Recent advances in deep learning models: a systematic literature review
In recent years, deep learning has evolved as a rapidly growing and stimulating field of machine learning and has redefined state-of-the-art...
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Recursive-learning-based moving object detection in video with dynamic environment
Moving object detection is a fundamental and critical task in video surveillance systems. It is very challenging for complex scenes having...
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A Sparse Online Approach for Streaming Data Classification via Prototype-Based Kernel Models
Processing big data streams through machine learning algorithms has various challenges, such as little time to train the models, hardware memory...
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A Performance Comparison of Robust Models in Wind Turbines Power Curve Estimation: A Case Study
The power curve modeling for wind turbines is a key tool used to predict the generated electric power, and to monitor and operate wind turbines,...
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AAIA: an efficient aggregation scheme against inverting attack for federated learning
Federated learning is emerged as an attractive paradigm regarding the data privacy problem, clients train the deep neural network on their local...
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Algorithmically Expressive, Always-Terminating Model for Reversible Computation
Concerning classical computational models able to express all the Primitive Recursive Functions (PRF), there are interesting results regarding limits...
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Should I Stay or Should I Go
We present the Emi reasoner, based on a new interpretation of the tableau algorithm for reasoning with Description Logics with unique performance...
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The design and application of a diffusion tensor informed finite-element model for exploration of uniaxially prestressed muscle architecture in magnetic resonance imaging
The combination of finite-element models with medical imaging has been a valuable contribution to our understanding of tissue mechanics. In recent...
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Gradient leakage attacks in federated learning
Federated Learning (FL) improves the privacy of local training data by exchanging model updates (e.g., local gradients or updated parameters)....
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When Machine Learning Models Leak: An Exploration of Synthetic Training Data
We investigate an attack on a machine learning classifier that predicts the propensity of a person or household to move (i.e., relocate) in the next...