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Found 9 papers, 0 papers with code
PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloud
Experimental results on simulation and the actual IBM quantum computer both prove the ability of PristiQ to provide high security for the quantum data while maintaining the model performance in QML.
A Physics-guided Generative AI Toolkit for Geophysical Monitoring
Full-waveform inversion (FWI) plays a vital role in geoscience to explore the subsurface.
TrojFair: Trojan Fairness Attacks
TrojFair is a stealthy Fairness attack that is resilient to existing model fairness audition detectors since the model for clean inputs is fair.
Muffin: A Framework Toward Multi-Dimension AI Fairness by Uniting Off-the-Shelf Models
Model fairness (a. k. a., bias) has become one of the most critical problems in a wide range of AI applications.
On-Device Unsupervised Image Segmentation
What's more, for a larger size image in the BBBC005 dataset, the existing approach cannot be accommodated to Raspberry PI due to out of memory; on the other hand, SegHDC can obtain segmentation results within 3 minutes while achieving a 0. 9587 IoU score.
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis
Self-supervised learning (SSL) methods, contrastive learning (CL) and masked autoencoders (MAE), can leverage the unlabeled data to pre-train models, followed by fine-tuning with limited labels.
The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices
Results show that FaHaNa can identify a series of neural networks with higher fairness and accuracy on a dermatology dataset.
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning
The recently developed self-supervised learning approach, contrastive learning (CL), can leverage the unlabeled data to pre-train a model, after which the model is fine-tuned on limited labeled data for dermatological disease diagnosis.
Automated Architecture Search for Brain-inspired Hyperdimensional Computing
On the Clintox dataset, which tries to learn features from developed drugs that passed/failed clinical trials for toxicity reasons, the searched HDC architecture obtains the state-of-the-art ROC-AUC scores, which are 0. 80% higher than the manually designed HDC and 9. 75% higher than conventional neural networks.
