| Accurate neuron resilience prediction for a flexible reliability management in neural network accelerators C Schorn, A Guntoro, G Ascheid 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), 979-984, 2018 | 64 | 2018 |
| SELD-TCN: Sound event localization & detection via temporal convolutional networks K Guirguis, C Schorn, A Guntoro, S Abdulatif, B Yang 2020 28th European Signal Processing Conference (EUSIPCO), 16-20, 2021 | 63 | 2021 |
| Efficient on-line error detection and mitigation for deep neural network accelerators C Schorn, A Guntoro, G Ascheid Computer Safety, Reliability, and Security: 37th International Conference …, 2018 | 52 | 2018 |
| An efficient bit-flip resilience optimization method for deep neural networks C Schorn, A Guntoro, G Ascheid 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE …, 2019 | 50 | 2019 |
| Automated design of error-resilient and hardware-efficient deep neural networks C Schorn, T Elsken, S Vogel, A Runge, A Guntoro, G Ascheid Neural Computing and Applications 32, 18327-18345, 2020 | 46 | 2020 |
| Facer: A universal framework for detecting anomalous operation of deep neural networks C Schorn, L Gauerhof 2020 IEEE 23rd International Conference on Intelligent Transportation …, 2020 | 12 | 2020 |
| Efficient stochastic inference of bitwise deep neural networks S Vogel, C Schorn, A Guntoro, G Ascheid arXiv preprint arXiv:1611.06539, 2016 | 12 | 2016 |
| Fault Injectors for TensorFlow: evaluation of the impact of random hardware faults on deep CNNs M Beyer, A Morozov, E Valiev, C Schorn, L Gauerhof, K Ding, K Janschek arXiv preprint arXiv:2012.07037, 2020 | 11 | 2020 |
| Bayesian Model for Trustworthiness Analysis of Deep Learning Classifiers. A Morozov, E Valiev, M Beyer, K Ding, L Gauerhof, C Schorn AISafety@ IJCAI, 2020 | 5 | 2020 |
| Guaranteed compression rate for activations in cnns using a frequency pruning approach S Vogel, C Schorn, A Guntoro, G Ascheid 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), 296-299, 2019 | 5 | 2019 |
| Method for calculating an output of a neural network C Schorn, S Vogel US Patent 11,301,749, 2022 | 4 | 2022 |
| Automated hardening of deep neural network architectures M Beyer, C Schorn, T Fabarisov, A Morozov, K Janschek ASME International Mechanical Engineering Congress and Exposition 85697 …, 2021 | 4 | 2021 |
| Considering reliability of deep learning function to boost data suitability and anomaly detection L Gauerhof, Y Hagiwara, C Schorn, M Trapp 2020 IEEE International Symposium on Software Reliability Engineering …, 2020 | 2 | 2020 |
| Inference calculation for neural networks with protection against memory errors A Guntoro, C Schorn, J Pletinckx, LL Ecco, S Vogel US Patent App. 17/798,978, 2023 | 1 | 2023 |
| Method and device for verifying a neuron function in a neural network A Guntoro, A Runge, C Schorn, S Vogel, J Topp, J Schirmer US Patent 11,593,232, 2023 | 1 | 2023 |
| Method, device, and computer program for creating training data in a vehicle C Schorn, L Gauerhof US Patent App. 17/658,323, 2022 | 1 | 2022 |
| Method and device for operating a neural network in a memory-efficient manner A Guntoro, A Runge, C Schorn, J Topp, S Vogel, J Schirmer US Patent 11,715,019, 2023 | | 2023 |
| Selective deactivation of processing units for artificial neural networks J Schirmer, A Guntoro, A Runge, C Schorn, J Topp, S Vogel US Patent 11,698,672, 2023 | | 2023 |
| Method, device, and computer program for an uncertainty assessment of an image classification C Schorn, L Gauerhof US Patent App. 17/698,766, 2022 | | 2022 |
| Method and device for correcting erroneous neuron functions in a neural network A Guntoro, C Schorn, J Pletinckx, LL Ecco, S Vogel US Patent App. 17/328,761, 2021 | | 2021 |
