| Detail-Preserving Pooling in Deep Networks F Saeedan, N Weber, M Goesele, S Roth Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 115 | 2018 |
| Rapid, Detail-Preserving Image Downscaling N Weber, M Waechter, SC Amend, S Guthe, M Goesele ACM Transactions on Graphics (TOG) 35 (6), 205, 2016 | 69 | 2016 |
| Fast dynamic memory allocator for massively parallel architectures S Widmer, D Wodniok, N Weber, M Goesele Proceedings of the 6th Workshop on General Purpose Processor Using Graphics …, 2013 | 32 | 2013 |
| Auto-Tuning Complex Array Layouts for GPUs N Weber, M Goesele Proceedings of the 14th Eurographics Symposium on Parallel Graphics and …, 2014 | 17 | 2014 |
| MATOG: Array Layout Auto-Tuning for CUDA N Weber, M Goesele ACM Transactions on Architecture and Code Optimization (TACO) 14 (3), 28, 2017 | 16 | 2017 |
| BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism N Weber, F Schmidt, M Niepert, F Huici arXiv preprint arXiv:1804.08378, 2018 | 9 | 2018 |
| Adaptive GPU Array Layout Auto-Tuning N Weber, M Goesele Proceedings of the ACM Workshop on Software Engineering Methods for Parallel …, 2016 | 8 | 2016 |
| Guided profiling for auto-tuning array layouts on GPUs N Weber, SC Amend, M Goesele Proceedings of the 6th International Workshop on Performance Modeling …, 2015 | 6 | 2015 |
| Systems and methods for data compression in neural networks N Weber, F Huici, M Niepert US Patent App. 16/012,832, 2019 | 5 | 2019 |
| Prospect for Knowledge in Survey Data: An Artificial Neural Network Sensitivity Analysis P Weber, N Weber, M Goesele, R Kabst Social Science Computer Review 36 (5), 575-590, 2018 | 5 | 2018 |
| Sol: Transparent neural network acceleration platform N Weber Proceedings of Supercomputing, 2018 | 3 | 2018 |
| SOL: Effortless Device Support for AI Frameworks without Source Code Changes N Weber, F Huici 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet …, 2020 | 2 | 2020 |
| SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworks N Weber arXiv preprint arXiv:2205.10357, 2022 | 1 | 2022 |
| GPU Array Access Auto-Tuning N Weber Technische Universität Darmstadt, 2017 | 1 | 2017 |
| Computation graph optimization by partial evaluations N Weber, D Thuerck US Patent App. 17/572,740, 2023 | | 2023 |
| Full asynchronous execution queue for accelerator hardware N Weber US Patent 11,593,157, 2023 | | 2023 |
| ZERO-COPY SPARSE MATRIX FACTORIZATION SYNTHESIS FOR HETEROGENEOUS COMPUTE SYSTEMS D Thuerck, N Weber US Patent App. 17/519,630, 2023 | | 2023 |
| Acceleration of neural networks using depth-first processing N Weber, F Huici, M Niepert US Patent 11,429,855, 2022 | | 2022 |
| COMBINATION OF MULTIPLE DATA PROCESSING AND MACHINE LEARNING FRAMEWORKS FOR A TARGET HARDWARE N Weber US Patent App. 17/070,962, 2022 | | 2022 |
| Flynn’s Reconciliation: Automating the Register Cache Idiom for Cross-accelerator Programming D Thuerck, N Weber, R Bifulco ACM Transactions on Architecture and Code Optimization (TACO) 18 (3), 1-26, 2021 | | 2021 |
Michael GoeseleMeta Reality Labs ResearchVerified email at goesele.org
