Martin Mundt

发表

Avalanche: an End-to-End Library for Continual Learning

Simone Calderara, Davide Bacciu, Simone Scardapane, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Return of the normal distribution: Flexible deep continual learning with variational auto-encoders

H. Byun, Martin Mundt, Sungho Park, 2022, Neural Networks.

When Deep Classifiers Agree: Analyzing Correlations between Learning Order and Image Statistics

Visvanathan Ramesh, Martin Mundt, Iuliia Pliushch, 2021, ECCV.

Predictive Whittle networks for time series

Fabrizio G. Ventola, D. S. Dhami, K. Kersting, 2022, UAI.

A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning

Visvanathan Ramesh, Martin Mundt, Iuliia Pliushch, 2020, Neural Networks.

Subharmonic Mixing With Field-Effect Transistors: Theory and Experiment at 639 GHz High Above $f_{T}$

Viktor Krozer, Alvydas Lisauskas, Hartmut G. Roskos, 2013, IEEE Sensors Journal.

CMOS Integrated Antenna-Coupled Field-Effect Transistors for the Detection of Radiation From 0.2 to 4.3 THz

S. Winnerl, H. Roskos, L. Minkevičius, 2012, IEEE Transactions on Microwave Theory and Techniques.

CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparability

K. Kersting, Martin Mundt, Quentin Delfosse, 2021, ICLR.

Probabilistic Circuits That Know What They Don't Know

Fabrizio G. Ventola, Martin Mundt, K. Kersting, 2023, ArXiv.

Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition pdf

Visvanathan Ramesh, Sagnik Majumder, Martin Mundt, 2019, J. Imaging.

Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?

Visvanathan Ramesh, Sagnik Majumder, Martin Mundt, 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference

Martin Mundt, Iuliia Pliushch, Visvanathan Ramesh, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Anomaly detection for automotive visual signal transition estimation

Visvanathan Ramesh, Martin Mundt, Tobias Weis, 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

Masked Autoencoders are Efficient Continual Federated Learners

Martin Mundt, K. Kersting, Roshni Kamath, 2023, ArXiv.

Antenna-coupled field-effect transistors for multi-spectral terahertz imaging up to 4.25 THz.

H. Roskos, L. Minkevičius, I. Kašalynas, 2014, Optics express.

Bow-tie-antenna-coupled terahertz detectors using AlGaN/GaN field-effect transistors with 0.25 micrometer gate length

H. Roskos, J. Wurfl, W. Heinrich, 2013, European Microwave Integrated Circuits Conference.

Deep Classifier Mimicry without Data Access

Martin Mundt, K. Kersting, Steven Braun, 2023, ArXiv.

A Procedural World Generation Framework for Systematic Evaluation of Continual Learning

Visvanathan Ramesh, Martin Mundt, Iuliia Pliushch, 2021, NeurIPS Datasets and Benchmarks.

Meta-Learning Convolutional Neural Architectures for Multi-Target Concrete Defect Classification With the COncrete DEfect BRidge IMage Dataset

Visvanathan Ramesh, Sagnik Majumder, Martin Mundt, 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Towards Coreset Learning in Probabilistic Circuits

K. Kersting, A. Solin, M. Trapp, 2022 .

Exploration of Terahertz Imaging with Silicon MOSFETs

E. Socher, H. Roskos, L. Minkevičius, 2014 .

Adaptive Rational Activations to Boost Deep Reinforcement Learning

Alejandro Molina, P. Schramowski, Martin Mundt, 2021, 2102.09407.

Probabilistic Circuits That Know What They Don't Know

Fabrizio G. Ventola, Martin Mundt, K. Kersting, 2023, UAI.

Continual Causality: A Retrospective of the Inaugural AAAI-23 Bridge Program

Tyler L. Hayes, Martin Mundt, 2023, AAAI Bridge Program.

Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation

Visvanathan Ramesh, Martin Mundt, Achref Jaziri, 2023, ArXiv.

BOWLL: A Deceptively Simple Open World Lifelong Learner

Martin Mundt, Roshni Kamath, Subarnaduti Paul, 2024, ArXiv.

Self Expanding Neural Networks

Martin Mundt, K. Kersting, R. Mitchell, 2023, ArXiv.