Packed Shamir secret sharing yields up to 11x lower communication and 2.6x faster online runtime for secure deep learning inference versus prior Shamir-based methods.
Gradient-based learning applied to document recognition
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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FLARE uses adaptive multi-dimensional reputation scores and soft exclusion to improve Byzantine robustness in federated learning by up to 16% over prior methods while handling a new Statistical Mimicry attack.
Recovery patterns that naturally occur across pixels in images restrict color recovery in scattering media to a unique solution for an ideal hyperspectral camera.
Enforcing feature decorrelation during training produces sharper saliency maps and higher accuracy on image classification benchmarks.
DyMETER unifies hypernetwork-driven parameter adaptation and dynamic thresholding for online anomaly detection under concept drift.
Natural Selection (NS) dynamically reweights DNN training losses by estimating each sample's competitive status inside groups assembled as composite images.
A multi-encoder fusion-attention wave network based on transformers combines Wi-Fi and 5G data for passive ISAC indoor scene inference, with a real prototype achieving localization errors below 0.6 m in 84% of cases.
Perceptual quality metrics correlate strongly with each other but show minimal correlation with attack success rate across medical imaging models and datasets, making ASR alone inadequate for assessing adversarial robustness.
citing papers explorer
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FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning
FLARE uses adaptive multi-dimensional reputation scores and soft exclusion to improve Byzantine robustness in federated learning by up to 16% over prior methods while handling a new Statistical Mimicry attack.
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FAWN: A MultiEncoder Fusion-Attention Wave Network for Integrated Sensing and Communication Indoor Scene Inference
A multi-encoder fusion-attention wave network based on transformers combines Wi-Fi and 5G data for passive ISAC indoor scene inference, with a real prototype achieving localization errors below 0.6 m in 84% of cases.