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ORCIDMartin Mundt
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- affiliation: TU Darmstadt, Germany
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2020 – today
- 2024
[j5]Eli Verwimp, Rahaf Aljundi, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H. Lampert, Martin Mundt, Razvan Pascanu, Adrian Popescu, Andreas S. Tolias, Joost van de Weijer, Bing Liu, Vincenzo Lomonaco, Tinne Tuytelaars, Gido M. van de Ven:
Continual Learning: Applications and the Road Forward. Trans. Mach. Learn. Res. 2024 (2024)- [c23]Steven Braun, Martin Mundt, Kristian Kersting:
Deep Classifier Mimicry without Data Access. AISTATS 2024: 4762-4770 - [c22]Quentin Delfosse, Patrick Schramowski, Martin Mundt, Alejandro Molina, Kristian Kersting:
Adaptive Rational Activations to Boost Deep Reinforcement Learning. ICLR 2024 - [c21]Nick Lemke, Camila González, Anirban Mukhopadhyay, Martin Mundt:
Distribution-Aware Replay for Continual MRI Segmentation. AIPAD/PILM@MICCAI 2024: 73-85 - [c20]Achref Jaziri, Martin Mundt, Andres Fernandez Rodriguez, Visvanathan Ramesh:
Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation. WACV 2024: 8621-8631 - [i22]Roshni Kamath, Rupert Mitchell, Subarnaduti Paul, Kristian Kersting, Martin Mundt:
BOWLL: A Deceptively Simple Open World Lifelong Learner. CoRR abs/2402.04814 (2024) - [i21]Florian Peter Busch, Roshni Kamath, Rupert Mitchell, Wolfgang Stammer, Kristian Kersting, Martin Mundt:
Where is the Truth? The Risk of Getting Confounded in a Continual World. CoRR abs/2402.06434 (2024) - [i20]Nick Lemke, Camila González, Anirban Mukhopadhyay, Martin Mundt:
Distribution-Aware Replay for Continual MRI Segmentation. CoRR abs/2407.21216 (2024) - [i19]Subarnaduti Paul, Manuel Brack, Patrick Schramowski, Kristian Kersting, Martin Mundt:
Core Tokensets for Data-efficient Sequential Training of Transformers. CoRR abs/2410.05800 (2024) - 2023
- [j4]Martin Mundt
, Yongwon Hong, Iuliia Pliushch, Visvanathan Ramesh:
A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning. Neural Networks 160: 306-336 (2023) - [c19]Martin Mundt, Keiland W. Cooper, Devendra Singh Dhami, Adèle H. Ribeiro, James Seale Smith, Alexis Bellot, Tyler L. Hayes:
Continual Causality: A Retrospective of the Inaugural AAAI-23 Bridge Program. AAAI Bridge Program 2023: 1-10 - [c18]Adrian Lutsch, Gagandeep Singh, Martin Mundt, Ragnar Mogk, Carsten Binnig:
Benchmarking the Second Generation of Intel SGX for Machine Learning Workloads. BTW 2023: 711-717 - [c17]Organizers Of QueerInAI, Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas Voelcker, Danica J. Sutherland, Davide Locatelli, Eva Breznik, Filip Klubicka, Hang Yuan, Hetvi Jethwani, Huan Zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Milind Agarwal, Nyx McLean, Pan Xu, Pranav A, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, St John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew McNamara, Raphael Gontijo Lopes, Alex Markham, Evyn Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark:
Queer In AI: A Case Study in Community-Led Participatory AI. FAccT 2023: 1882-1895 - [c16]Fabrizio Ventola, Steven Braun, Zhongjie Yu, Martin Mundt, Kristian Kersting:
Probabilistic circuits that know what they don't know. UAI 2023: 2157-2167 - [e1]Martin Mundt, Keiland W. Cooper, Devendra Singh Dhami, Adèle H. Ribeiro, James Seale Smith, Alexis Bellot, Tyler L. Hayes:
AAAI Bridge Program on Continual Causality, 7-8 February 2023, Washington, DC, USA. Proceedings of Machine Learning Research 208, PMLR 2023 [contents] - [i18]Fabrizio Ventola, Steven Braun, Zhongjie Yu, Martin Mundt, Kristian Kersting:
Probabilistic Circuits That Know What They Don't Know. CoRR abs/2302.06544 (2023) - [i17]Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas Voelcker, Danica J. Sutherland, Davide Locatelli, Eva Breznik, Filip Klubicka, Hang Yuan, Hetvi Jethwani, Huan Zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Milind Agarwal, Nyx McLean, Pan Xu, Pranav A, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, St John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew McNamara, Raphael Gontijo Lopes, Alex Markham, Evyn Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark:
Queer In AI: A Case Study in Community-Led Participatory AI. CoRR abs/2303.16972 (2023) - [i16]Steven Braun, Martin Mundt, Kristian Kersting:
Deep Classifier Mimicry without Data Access. CoRR abs/2306.02090 (2023) - [i15]Subarnaduti Paul, Lars-Joel Frey, Roshni Kamath, Kristian Kersting, Martin Mundt:
Masked Autoencoders are Efficient Continual Federated Learners. CoRR abs/2306.03542 (2023) - [i14]Rupert Mitchell, Martin Mundt, Kristian Kersting:
Self Expanding Neural Networks. CoRR abs/2307.04526 (2023) - [i13]Achref Jaziri, Martin Mundt, Andres Fernandez Rodriguez, Visvanathan Ramesh:
Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation. CoRR abs/2309.09637 (2023) - [i12]Eli Verwimp, Rahaf Aljundi, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H. Lampert, Martin Mundt, Razvan Pascanu, Adrian Popescu, Andreas S. Tolias, Joost van de Weijer, Bing Liu, Vincenzo Lomonaco, Tinne Tuytelaars, Gido M. van de Ven:
Continual Learning: Applications and the Road Forward. CoRR abs/2311.11908 (2023) - 2022
- [j3]Mara Kaufeld, Martin Mundt, Sarah Forst, Heiko Hecht:
Optical see-through augmented reality can induce severe motion sickness. Displays 74: 102283 (2022) - [j2]Martin Mundt, Iuliia Pliushch, Sagnik Majumder, Yong Won Hong, Visvanathan Ramesh:
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition. J. Imaging 8(4): 93 (2022) - [j1]Yongwon Hong, Martin Mundt, Sungho Park, Yungjung Uh, Hyeran Byun:
Return of the normal distribution: Flexible deep continual learning with variational auto-encoders. Neural Networks 154: 397-412 (2022) - [c15]Iuliia Pliushch, Martin Mundt, Nicolas Lupp, Visvanathan Ramesh:
When Deep Classifiers Agree: Analyzing Correlations Between Learning Order and Image Statistics. ECCV (8) 2022: 397-413 - [c14]Martin Mundt, Steven Lang, Quentin Delfosse, Kristian Kersting:
CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability. ICLR 2022 - [c13]Zhongjie Yu, Fabrizio Ventola, Nils Thoma, Devendra Singh Dhami, Martin Mundt, Kristian Kersting:
Predictive Whittle networks for time series. UAI 2022: 2320-2330 - 2021
- [b1]Martin Mundt:
Designing deep neural networks for continual learning in an open world. Goethe University Frankfurt, Frankfurt am Main, Germany, 2021 - [c12]Martin Mundt, Iuliia Pliushch, Visvanathan Ramesh:
Neural Architecture Search of Deep Priors: Towards Continual Learning Without Catastrophic Interference. CVPR Workshops 2021: 3523-3532 - [c11]Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Antonio Carta, Gabriele Graffieti, Tyler L. Hayes, Matthias De Lange, Marc Masana, Jary Pomponi, Gido M. van de Ven, Martin Mundt, Qi She, Keiland W. Cooper, Jeremy Forest, Eden Belouadah, Simone Calderara, German Ignacio Parisi, Fabio Cuzzolin, Andreas S. Tolias, Simone Scardapane, Luca Antiga, Subutai Ahmad, Adrian Popescu, Christopher Kanan, Joost van de Weijer, Tinne Tuytelaars, Davide Bacciu, Davide Maltoni:
Avalanche: An End-to-End Library for Continual Learning. CVPR Workshops 2021: 3600-3610 - [c10]Timm Hess, Martin Mundt, Iuliia Pliushch, Visvanathan Ramesh:
A Procedural World Generation Framework for Systematic Evaluation of Continual Learning. NeurIPS Datasets and Benchmarks 2021 - [c9]Steven Lang, Martin Mundt, Fabrizio Ventola, Robert Peharz, Kristian Kersting:
Elevating Perceptual Sample Quality in PCs through Differentiable Sampling. Pre-Registration Workshop @ NeurIPS 2021: 1-25 - [i11]Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Antonio Carta, Gabriele Graffieti, Tyler L. Hayes, Matthias De Lange, Marc Masana, Jary Pomponi, Gido M. van de Ven, Martin Mundt, Qi She, Keiland W. Cooper, Jeremy Forest, Eden Belouadah, Simone Calderara, German Ignacio Parisi, Fabio Cuzzolin, Andreas S. Tolias, Simone Scardapane, Luca Antiga, Subutai Amhad, Adrian Popescu, Christopher Kanan, Joost van de Weijer, Tinne Tuytelaars, Davide Bacciu, Davide Maltoni:
Avalanche: an End-to-End Library for Continual Learning. CoRR abs/2104.00405 (2021) - [i10]Martin Mundt, Iuliia Pliushch, Visvanathan Ramesh:
Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference. CoRR abs/2104.06788 (2021) - [i9]Iuliia Pliushch, Martin Mundt, Nicolas Lupp, Visvanathan Ramesh:
When Deep Classifiers Agree: Analyzing Correlations between Learning Order and Image Statistics. CoRR abs/2105.08997 (2021) - [i8]Timm Hess, Martin Mundt, Iuliia Pliushch, Visvanathan Ramesh:
A Procedural World Generation Framework for Systematic Evaluation of Continual Learning. CoRR abs/2106.02585 (2021) - [i7]Martin Mundt, Steven Lang, Quentin Delfosse, Kristian Kersting:
CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparability. CoRR abs/2110.03331 (2021) - 2020
- [c8]Martin Mundt, Tintu Mathew:
An Evaluation of Pie Menus for System Control in Virtual Reality. NordiCHI 2020: 12:1-12:8 - [i6]Martin Mundt, Yong Won Hong, Iuliia Pliushch, Visvanathan Ramesh:
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning. CoRR abs/2009.01797 (2020)
2010 – 2019
- 2019
- [c7]Martin Mundt, Sagnik Majumder, Sreenivas Murali, Panagiotis Panetsos, Visvanathan Ramesh:
Meta-Learning Convolutional Neural Architectures for Multi-Target Concrete Defect Classification With the COncrete DEfect BRidge IMage Dataset. CVPR 2019: 11196-11205 - [c6]Martin Mundt, Iuliia Pliushch, Sagnik Majumder, Visvanathan Ramesh:
Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers? ICCV Workshops 2019: 753-757 - [c5]Martin Mundt, Tintu Mathew:
Exploring Pie Menus for System Control Tasks in Virtual Reality. MuC 2019: 509-513 - [i5]Martin Mundt, Sagnik Majumder, Sreenivas Murali, Panagiotis Panetsos, Visvanathan Ramesh:
Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset. CoRR abs/1904.08486 (2019) - [i4]Martin Mundt, Sagnik Majumder, Iuliia Pliushch, Visvanathan Ramesh:
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition. CoRR abs/1905.12019 (2019) - [i3]Martin Mundt, Iuliia Pliushch, Sagnik Majumder, Visvanathan Ramesh:
Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers? CoRR abs/1908.09625 (2019) - 2018
- [i2]Martin Mundt, Sagnik Majumder, Tobias Weis, Visvanathan Ramesh:
Rethinking Layer-wise Feature Amounts in Convolutional Neural Network Architectures. CoRR abs/1812.05836 (2018) - 2017
- [c4]Helge Fischer, Matthias Heinz, Christian Leyh, Marko Ott, Sandra Döring, Cornelia Schade, Annika Löser, Martin Mundt, Anne Trojanek, Holger Rohland:
Lernst du noch oder spielst du schon? Zum Einsatz von GameDesign-Elementen in der Hochschullehre.(Are you still Learning or Already Gaming? The Usage of Game Design Elements in University Teaching). DeLFI/GMW Workshops 2017 - [c3]Tobias Weis, Martin Mundt, Patrick Harding, Visvanathan Ramesh:
Anomaly detection for automotive visual signal transition estimation. ITSC 2017: 1-8 - [c2]Timm Hess, Martin Mundt, Tobias Weis, Visvanathan Ramesh:
Large-Scale Stochastic Scene Generation and Semantic Annotation for Deep Convolutional Neural Network Training in the RoboCup SPL. RoboCup 2017: 33-44 - [i1]Martin Mundt, Tobias Weis, Kishore Konda, Visvanathan Ramesh:
Building effective deep neural network architectures one feature at a time. CoRR abs/1705.06778 (2017) - 2016
- [c1]Martin Mundt, Sebastian Blaes, Thomas Burwick:
Feature binding in deep convolution networks with recurrences, oscillations, and top-down modulated dynamics. ESANN 2016
Coauthor Index
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