Nicolas Weber
TitleCited byYear
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
222013
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
202018
Rapid, Detail-Preserving Image Downscaling
N Weber, M Waechter, SC Amend, S Guthe, M Goesele
ACM Transactions on Graphics (TOG) 35 (6), 205, 2016
162016
Auto-Tuning Complex Array Layouts for GPUs
N Weber, M Goesele
Proceedings of the 14th Eurographics Symposium on Parallel Graphics and …, 2014
112014
MATOG: Array Layout Auto-Tuning for CUDA
N Weber, M Goesele
ACM Transactions on Architecture and Code Optimization (TACO) 14 (3), 28, 2017
82017
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
52015
Adaptive GPU Array Layout Auto-Tuning
N Weber, M Goesele
Proceedings of the ACM Workshop on Software Engineering Methods for Parallel …, 2016
42016
BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism
N Weber, F Schmidt, M Niepert, F Huici
arXiv preprint arXiv:1804.08378, 2018
22018
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
2018
GPU Array Access Auto-Tuning
N Weber
Technische Universität Darmstadt, 2017
2017
Fast Dynamic Memory Allocator for Massively Parallel Architectures
D Wodniok, S Widmer, N Weber, M Goesele
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Articles 1–11