Oct 2024 // book-chapter
Distribution-Aware Replay for Continual MRI Segmentation
Lecture Notes in Computer Science
Nick Lemke; Camila González; Anirban Mukhopadhyay; Martin Mundt
2024 // other
BOWLL: A Deceptively Simple Open World Lifelong Learner
arXiv
Kamath, R.; Mitchell, R.; Paul, S.; Kersting, K.; Mundt, M.
DOI: 10.48550/arXiv.2402.04814
2024 // conference-paper
Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation
Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
Jaziri, A.; Mundt, M.; Rodriguez, A.F.; Ramesh, V.
DOI: 10.1109/WACV57701.2024.00844
2024 // other
CORE TOKENSETS FOR DATA-EFFICIENT SEQUENTIAL TRAINING OF TRANSFORMERS
arXiv
Paul, S.; Brack, M.; Schramowski, P.; Kersting, K.; Mundt, M.
DOI: 10.48550/arXiv.2410.05800
2024 // conference-paper
ADAPTIVE RATIONAL ACTIVATIONS TO BOOST DEEP REINFORCEMENT LEARNING
12th International Conference on Learning Representations, ICLR 2024
Delfosse, Q.; Schramowski, P.; Mundt, M.; Molina, A.; Kersting, K.
DOI:
2024 // conference-paper
Deep Classifier Mimicry without Data Access
Proceedings of Machine Learning Research
Braun, S.; Mundt, M.; Kersting, K.
DOI:
2024 // other
Where is the Truth? The Risk of Getting Confounded in a Continual World
arXiv
Busch, F.P.; Kamath, R.R.; Mitchell, R.; Stammer, W.; Kersting, K.; Mundt, M.
DOI: 10.48550/arXiv.2402.06434
Mar 2023 // journal-article
A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning
Neural Networks
Martin Mundt; Yongwon Hong; Iuliia Pliushch; Visvanathan Ramesh
2023 // conference-paper
Queer In AI: A Case Study in Community-Led Participatory AI
ACM International Conference Proceeding Series
Queerinai, O.O.; Ovalle, A.; Subramonian, A.; Singh, A.; Voelcker, C.; Sutherland, D.J.; Locatelli, D.; Breznik, E.; Klubicka, F.; Yuan, H.; Hetvi, J.; Zhang, H.; Shriram, J.; Lehman, K.; Soldaini, L.; Sap, M.; Deisenroth, M.P.; Pacheco, M.L.; Ryskina, M.; Mundt, M.; Agarwal, M.; Mclean, N.; Xu, P.; Pranav, A.; Korpan, R.; Ray, R.; Mathew, S.; Arora, S.; John, S.; Anand, T.; Agrawal, V.; Agnew, W.; Long, Y.; Wang, Z.J.; Talat, Z.; Ghosh, A.; Dennler, N.; Noseworthy, M.; Jha, S.; Baylor, E.; Joshi, A.; Bilenko, N.Y.; Mcnamara, A.; Gontijo-Lopes, R.; Markham, A.; Dong, E.; Kay, J.; Saraswat, M.; Vytla, N.; Stark, L.
DOI: 10.1145/3593013.3594134
2023 // conference-paper
Probabilistic Circuits That Know What They Don't Know
Proceedings of Machine Learning Research
Ventola, F.; Braun, S.; Yu, Z.; Mundt, M.; Kersting, K.
DOI:
2023 // other
MASKED AUTOENCODERS ARE EFFICIENT CONTINUAL FEDERATED LEARNERS
arXiv
Paul, S.; Frey, L.-J.; Kamath, R.; Kersting, K.; Mundt, M.
DOI: 10.48550/arXiv.2306.03542
2023 // other
Self-Expanding Neural Networks
arXiv
Mitchell, R.; Menzenbach, R.; Kersting, K.; Mundt, M.
DOI: 10.48550/arXiv.2307.04526
2023 // other
Continual Learning: Applications and the Road Forward
arXiv
Verwimp, E.; Aljundi, R.; Ben-David, S.; Bethge, M.; Cossu, A.; Gepperth, A.; Hayes, T.L.; Hüllermeier, E.; Kanan, C.; Kudithipudi, D.; Lampert, C.H.; Mundt, M.; Pascanu, R.; Popescu, A.; Tolias, A.S.; van de Weijer, J.; Liu, B.; Lomonaco, V.; Tuytelaars, T.; van de Ven, G.M.
DOI: 10.48550/arXiv.2311.11908
2023 // conference-paper
Benchmarking the Second Generation of Intel SGX for Machine Learning Workloads
Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)
Lutsch, A.; Singh, G.; Mundt, M.; Mogk, R.; Binnig, C.
DOI: 10.18420/BTW2023-44
2023 // conference-paper
Continual Causality: A Retrospective of the Inaugural AAAI-23 Bridge Program
Proceedings of Machine Learning Research
Mundt, M.; Cooper, K.W.; Dhami, D.S.; Ribeiro, A.; Smith, J.S.; Bellot, A.; Hayes, T.
DOI:
Mar 2022 // journal-article
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
Journal of Imaging
Martin Mundt; Iuliia Pliushch; Sagnik Majumder; Yongwon Hong; Visvanathan Ramesh
2022 // journal-article
Return of the normal distribution: Flexible deep continual learning with variational auto-encoders
Neural Networks
Hong, Y.; Mundt, M.; Park, S.; Uh, Y.; Byun, H.
DOI: 10.1016/j.neunet.2022.07.016
2022 // conference-paper
When Deep Classifiers Agree: Analyzing Correlations Between Learning Order and Image Statistics
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pliushch, I.; Mundt, M.; Lupp, N.; Ramesh, V.
DOI: 10.1007/978-3-031-20074-8_23
2022 // other
FEATHERS: Federated Architecture and Hyperparameter Search
arXiv
Seng, J.; Prasad, P.; Dhami, D.S.; Mundt, M.; Kersting, K.
DOI: 10.48550/arXiv.2206.12342
2022 // conference-paper
Predictive Whittle Networks for Time Series
Proceedings of Machine Learning Research
Yu, Z.; Ventola, F.; Thoma, N.; Dhami, D.S.; Mundt, M.; Kersting, K.
DOI:
2022 // conference-paper
Elevating Perceptual Sample Quality in Probabilistic Circuits through Differentiable Sampling
Proceedings of Machine Learning Research
Lang, S.; Mundt, M.; Ventola, F.; Peharz, R.; Kersting, K.
DOI:
2022 // conference-paper
CLEVA-COMPASS: A CONTINUAL LEARNING EVALUATION ASSESSMENT COMPASS TO PROMOTE RESEARCH TRANSPARENCY AND COMPARABILITY
ICLR 2022 - 10th International Conference on Learning Representations
Mundt, M.; Lang, S.; Delfosse, Q.; Kersting, K.
DOI:
2021 // conference-paper
Neural architecture search of deep priors: Towards continual learning without catastrophic interference
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Mundt, M.; Pliushch, I.; Ramesh, V.
DOI: 10.1109/CVPRW53098.2021.00391
2021 // conference-paper
Avalanche: An end-to-end library for continual learning
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Lomonaco, V.; Pellegrini, L.; Cossu, A.; Carta, A.; Graffieti, G.; Hayes, T.L.; De Lange, M.; Masana, M.; Pomponi, J.; Van De Ven, G.M.; Mundt, M.; She, Q.; Cooper, K.; Forest, J.; Belouadah, E.; Calderara, S.; Parisi, G.I.; Cuzzolin, F.; Tolias, A.S.; Scardapane, S.; Antiga, L.; Ahmad, S.; Popescu, A.; Kanan, C.; Van De Weijer, J.; Tuytelaars, T.; Bacciu, D.; Maltoni, D.
DOI: 10.1109/CVPRW53098.2021.00399
2021 // conference-paper
A procedural world generation framework for systematic evaluation of continual learning
Neural Information Processing Systems, Datasets and Benchmarks Track
