IAM

DAVIDSTUTZ

CATEGORY»RESEARCH;FIRST-AUTHOR PROJECTS«

21thOCTOBER2021

PROJECT

End-to-end training of deep neural networks and conformal predictors to reduce confidence set size and optimizer application-specific objectives.

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27thJULY2021

PROJECT

Random and adversarial bit error robustness of DNNs for energy-efficient and secure DNN accelerators.

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27thJULY2021

PROJECT

Robust generalization and overfitting linked to flatness of robust loss surface in weight space.

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26thJUNE2020

PROJECT

Random and adversarial bit errors in quantized DNN weights.

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01stMARCH2020

PROJECT

Confidence calibration of adversarial training for “generalizable” robustness.

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04thDECEMBER2018

PROJECT

Disentangling the relationship between adversarial robustness and generalization.

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19thMAY2018

PROJECT

Learning 3D shape completion under weak supervision; on ShapeNet, ModelNet, KITTI and Kinect data; published at CVPR and on ArXiv.

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17thDECEMBER2017

PROJECT

Weakly-supervised shape completion of cars on KITTI using variational auto-encoders; including two synthetic ShapeNet-based benchmark datasets.

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29thJANUARY2017

PROJECT

Revised C++ implementations of two popular superpixel algorithms, SEEDS and FH, which are shown to outperform the original implementations.

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05thDECEMBER2016

PROJECT

A comprehensive comparison and evaluation of 28 superpixel algorithms on 5 different datasets; published in CVIU and GCPR.

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