Supervised learning approaches including kernel estimation, random forests, additive models, and deep learning are proposed to estimate conditional covariance matrices for removing multivariate environmental influences in SHM beyond standard response surface modeling.
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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
CRAM-ER combines spintronic computational RAM with CMOS adder trees and software fine-tuning to deliver near-lossless DNN accuracy at up to 100x lower latency than CPU/GPU baselines.
FlowGuard applies continuous normalizing flows to flag out-of-distribution synthetic queries from model stealing attacks on IDS, achieving stable detection in single-client and 100-client Sybil settings unlike identity-dependent baselines.
citing papers explorer
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Removal of Multivariate Environmental Influences in Structural Health Monitoring through Conditional Covariances and Supervised Learning
Supervised learning approaches including kernel estimation, random forests, additive models, and deep learning are proposed to estimate conditional covariance matrices for removing multivariate environmental influences in SHM beyond standard response surface modeling.
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CRAM-ER: Error-Resilient Spintronic Computational Random Access Memory for Scalable In-Memory Computation
CRAM-ER combines spintronic computational RAM with CMOS adder trees and software fine-tuning to deliver near-lossless DNN accuracy at up to 100x lower latency than CPU/GPU baselines.
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FlowGuard: Flow Matching for Identity-Independent Detection of Data-Free Model Stealing Attacks on Energy System Intrusion Detection Systems
FlowGuard applies continuous normalizing flows to flag out-of-distribution synthetic queries from model stealing attacks on IDS, achieving stable detection in single-client and 100-client Sybil settings unlike identity-dependent baselines.