Martin Mundt

Orcid: 0000-0003-1639-8255

Affiliations:
  • TU Darmstadt, Germany


According to our database1, Martin Mundt authored at least 40 papers between 2016 and 2024.

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Bibliography

2024
Continual Learning: Applications and the Road Forward.
Trans. Mach. Learn. Res., 2024

Core Tokensets for Data-efficient Sequential Training of Transformers.
CoRR, 2024

Where is the Truth? The Risk of Getting Confounded in a Continual World.
CoRR, 2024

BOWLL: A Deceptively Simple Open World Lifelong Learner.
CoRR, 2024

Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

Distribution-Aware Replay for Continual MRI Segmentation.
Proceedings of the Artificial Intelligence in Pancreatic Disease Detection and Diagnosis, and Personalized Incremental Learning in Medicine, 2024

Adaptive Rational Activations to Boost Deep Reinforcement Learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Deep Classifier Mimicry without Data Access.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning.
Neural Networks, March, 2023

Self Expanding Neural Networks.
CoRR, 2023

Masked Autoencoders are Efficient Continual Federated Learners.
CoRR, 2023

Queer In AI: A Case Study in Community-Led Participatory AI.
CoRR, 2023

Probabilistic circuits that know what they don't know.
Proceedings of the Uncertainty in Artificial Intelligence, 2023


Benchmarking the Second Generation of Intel SGX for Machine Learning Workloads.
Proceedings of the Datenbanksysteme für Business, 2023

Continual Causality: A Retrospective of the Inaugural AAAI-23 Bridge Program.
Proceedings of the AAAI Bridge Program on Continual Causality, 2023

2022
Return of the normal distribution: Flexible deep continual learning with variational auto-encoders.
Neural Networks, 2022

Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition.
J. Imaging, 2022

Optical see-through augmented reality can induce severe motion sickness.
Displays, 2022

Predictive Whittle networks for time series.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability.
Proceedings of the Tenth International Conference on Learning Representations, 2022

When Deep Classifiers Agree: Analyzing Correlations Between Learning Order and Image Statistics.
Proceedings of the Computer Vision - ECCV 2022, 2022

2021
Designing deep neural networks for continual learning in an open world.
PhD thesis, 2021

Avalanche: an End-to-End Library for Continual Learning.
CoRR, 2021

Elevating Perceptual Sample Quality in PCs through Differentiable Sampling.
Proceedings of the NeurIPS 2021 Workshop on Pre-Registration in Machine Learning, 2021

A Procedural World Generation Framework for Systematic Evaluation of Continual Learning.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Neural Architecture Search of Deep Priors: Towards Continual Learning Without Catastrophic Interference.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021


2020
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning.
CoRR, 2020

An Evaluation of Pie Menus for System Control in Virtual Reality.
Proceedings of the NordiCHI '20: Shaping Experiences, 2020

2019
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition.
CoRR, 2019

Exploring Pie Menus for System Control Tasks in Virtual Reality.
Proceedings of Mensch und Computer 2019, Hamburg, Germany, September 8-11, 2019, 2019

Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshops, 2019

Meta-Learning Convolutional Neural Architectures for Multi-Target Concrete Defect Classification With the COncrete DEfect BRidge IMage Dataset.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
Rethinking Layer-wise Feature Amounts in Convolutional Neural Network Architectures.
CoRR, 2018

2017
Building effective deep neural network architectures one feature at a time.
CoRR, 2017

Large-Scale Stochastic Scene Generation and Semantic Annotation for Deep Convolutional Neural Network Training in the RoboCup SPL.
Proceedings of the RoboCup 2017: Robot World Cup XXI [Nagoya, Japan, July 27-31, 2017]., 2017

Anomaly detection for automotive visual signal transition estimation.
Proceedings of the 20th IEEE International Conference on Intelligent Transportation Systems, 2017

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).
Proceedings of the Joint Proceedings of the Pre-Conference Workshops of DeLFI and GMW 2017 co-located with 15th e-Learning Conference of the German Computer Society (DeLFI 2017) and the 25th Annual Conference of the Gesellschaft für Medien in der Wissenschaft (GMW 2017), 2017

2016
Feature binding in deep convolution networks with recurrences, oscillations, and top-down modulated dynamics.
Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016


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