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Found 16 papers, 10 papers with code
A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures
As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies.
Learning Robust and Consistent Time Series Representations: A Dilated Inception-Based Approach
Representation learning for time series has been an important research area for decades.
FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection
Federated learning (FL) enables multiple clients to train a model without compromising sensitive data.
Multimodal contrastive learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals and patient metadata
This work discusses the use of contrastive learning and deep learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals.
High Accurate and Explainable Multi-Pill Detection Framework with Graph Neural Network-Assisted Multimodal Data Fusion
We then offer a framework for integrating a priori with pills' visual features to enhance detection accuracy.
Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions
Federated learning (FL) is a machine learning (ML) approach that allows the use of distributed data without compromising personal privacy.
CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularization
By performing an in-depth analysis of the behavior of a classification model's penultimate layer, we introduce a metric that quantifies the similarity between two clients' data distributions without violating their privacy.
FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Collaborative Training
FedDCT reduces the memory requirements and allows low-end devices to participate in FL.
Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction
Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data.
Multi-stream Fusion for Class Incremental Learning in Pill Image Classification
From this framework, we consider color-specific information of pill images as a guidance stream and devise an approach, namely "Color Guidance with Multi-stream intermediate fusion"(CG-IMIF) for solving CIL pill image classification task.
A Survey of Machine Unlearning
Specifically, as a category collection of cutting-edge studies, the intention behind this article is to serve as a comprehensive resource for researchers and practitioners seeking an introduction to machine unlearning and its formulations, design criteria, removal requests, algorithms, and applications.
A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive Learning
Medication mistaking is one of the risks that can result in unpredictable consequences for patients.
A Deep Reinforcement Learning-based Adaptive Charging Policy for Wireless Rechargeable Sensor Networks
We then use a deep neural network to parametrize this charging policy, which will be trained by reinforcement learning techniques.
FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning
The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning.
Image-based Contextual Pill Recognition with Medical Knowledge Graph Assistance
To this end, in this paper, we introduce a novel approach named PIKA that leverages external knowledge to enhance pill recognition accuracy.
SAFL: A Self-Attention Scene Text Recognizer with Focal Loss
In this paper, we introduce SAFL, a self-attention-based neural network model with the focal loss for scene text recognition, to overcome the limitation of the existing approaches.
Ranked #4 on
Scene Text Recognition
on ICDAR 2003
Optical Character Recognition
Optical Character Recognition (OCR)
+1
