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Hierarchical Feature Extraction for Efficient Design of Microfluidic Flow Patterns.

, , , , and . FE@NIPS, volume 44 of JMLR Workshop and Conference Proceedings, page 213-225. JMLR.org, (2015)

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Hierarchical Feature Extraction for Efficient Design of Microfluidic Flow Patterns., , , , and . FE@NIPS, volume 44 of JMLR Workshop and Conference Proceedings, page 213-225. JMLR.org, (2015)Topology control in mobile sensor networks using information space feedback., , and . ACC, page 6405-6410. IEEE, (2016)Physics-Based Features for Anomaly Detection in Power Grids with Micro-PMUs., , , and . ICC, page 1-7. IEEE, (2018)Implicit Context-aware Learning and Discovery for Streaming Data Analytics., and . CoRR, (2019)Data-driven root-cause analysis for distributed system anomalies., , and . CDC, page 5745-5750. IEEE, (2017)LLNet: A deep autoencoder approach to natural low-light image enhancement., , and . Pattern Recognit., (2017)LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement., , and . CoRR, (2015)Multimodal spatiotemporal information fusion using neural-symbolic modeling for early detection of combustion instabilities., , , , and . ACC, page 4918-4923. IEEE, (2016)Deep Action Sequence Learning for Causal Shape Transformation., , , , and . CoRR, (2016)Deep Value of Information Estimators for Collaborative Human-Machine Information Gathering., , , , and . ICCPS, page 3:1-3:10. IEEE Computer Society, (2016)