Martin Mundt
Martin Mundt
DEPTH research group leader at hessian.AI and TU Darmstadt. Board member at ContinualAI
Geverifieerd e-mailadres voor tu-darmstadt.de - Homepage
Geciteerd door
Geciteerd door
CMOS integrated antenna-coupled field-effect transistors for the detection of radiation from 0.2 to 4.3 THz
S Boppel, A Lisauskas, M Mundt, D Seliuta, L Minkevicius, I Kasalynas, ...
IEEE transactions on microwave theory and techniques 60 (12), 3834-3843, 2012
Exploration of terahertz imaging with silicon MOSFETs
A Lisauskas, M Bauer, S Boppel, M Mundt, B Khamaisi, E Socher, ...
Journal of Infrared, Millimeter, and Terahertz Waves 35 (1), 63-80, 2014
Antenna-coupled field-effect transistors for multi-spectral terahertz imaging up to 4.25 THz
M Bauer, R Venckevičius, I Kašalynas, S Boppel, M Mundt, L Minkevičius, ...
Optics express 22 (16), 19235-19241, 2014
Subharmonic Mixing With Field-Effect Transistors: Theory and Experiment at 639 GHz High Above
A Lisauskas, S Boppel, M Mundt, V Krozer, HG Roskos
IEEE Sensors Journal 13 (1), 124-132, 2012
Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset
M Mundt, S Majumder, S Murali, P Panetsos, V Ramesh
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Avalanche: an end-to-end library for continual learning
V Lomonaco, L Pellegrini, A Cossu, A Carta, G Graffieti, TL Hayes, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning
M Mundt, YW Hong, I Pliushch, V Ramesh
arXiv preprint arXiv:2009.01797, 2020
Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?
M Mundt, I Pliushch, S Majumder, V Ramesh
International Conference on Computer Vision (ICCV) 2019, Workshop on …, 2019
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
M Mundt, I Pliushch, S Majumder, Y Hong, V Ramesh
Journal of Imaging, Special Issue Continual Learning in Computer Vision …, 2022
Bow-tie-antenna-coupled terahertz detectors using AlGaN/GaN field-effect transistors with 0.25 micrometer gate length
M Bauer, A Lisauskas, S Boppel, M Mundt, V Krozer, HG Roskos, ...
2013 European Microwave Integrated Circuit Conference, 212-215, 2013
Large-scale Stochastic Scene Generation and Semantic Annotation for Deep Convolutional Neural Network Training in the RoboCup SPL
T Hess*, M Mundt*, T Weis, V Ramesh, (* equal contribution)
RoboCup 2017: Robot World CUP XXI, LNAI 11175, 2017
Anomaly Detection for Automotive Visual Signal Transition Estimation
T Weis, M Mundt, P Harding, V Ramesh
20th IEEE Intelligent Transportation Systems Conference (ITSC), 2017
Optimized Tera-FET detector performance based on an analytical device model verified up to 9 THz
S Boppel, A Lisauskas, M Bauer, M Mundt, R Venckevičius, L Minkevičius, ...
2013 38th International Conference on Infrared, Millimeter, and Terahertz …, 2013
Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference
M Mundt, I Pliushch, V Ramesh
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparability
M Mundt, S Lang, Q Delfosse, K Kersting
International Conference on Learning Representations (ICLR), 2022
When deep classifiers agree: Analyzing correlations between learning order and image statistics
I Pliushch, M Mundt, N Lupp, V Ramesh
arXiv preprint arXiv:2105.08997, 2021
All-electronic terahertz imaging: planar emitters and detectors at 220 GHz in CMOS technology
A Lisauskas, B Khamaisi, S Boppel, M Mundt, V Krozer, E Socher, ...
2012 37th International Conference on Infrared, Millimeter, and Terahertz …, 2012
Adaptive Rational Activations to Boost Deep Reinforcement Learning
Q Delfosse, P Schramowski, M Mundt, A Molina, K Kersting
arXiv preprint arXiv:2102.09407, 2021
Rethinking Layer-wise Feature Amounts in Convolutional Neural Network Architectures
M Mundt, S Majumder, T Weis, V Ramesh
NeurIPS 2018 Critiquing and Correcting Trends in Machine Learning (CRACT …, 2018
Building effective deep neural network architectures one feature at a time
M Mundt, T Weis, K Konda, V Ramesh
https://arxiv.org/abs/1705.06778, 2017
Het systeem kan de bewerking nu niet uitvoeren. Probeer het later opnieuw.
Artikelen 1–20