Feature binding in deep convolution networks with recurrences, oscillations, and top-down modulated dynamics. - Martin Mundt; Sebastian Blaes; Thomas Burwick (ESANN, 2016)

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). - Helge Fischer; Matthias Heinz; Christian Leyh; Marko Ott; Sandra Döring; Cornelia Schade; Annika Löser; Martin Mundt; Anne Trojanek; Holger Rohland (DeLFI/GMW Workshops, 2017)

Anomaly detection for automotive visual signal transition estimation. - Tobias Weis; Martin Mundt; Patrick Harding; Visvanathan Ramesh (ITSC, 2017)

Large-Scale Stochastic Scene Generation and Semantic Annotation for Deep Convolutional Neural Network Training in the RoboCup SPL. - Timm Hess; Martin Mundt; Tobias Weis; Visvanathan Ramesh (RoboCup, 2017)

Building effective deep neural network architectures one feature at a time. - Martin Mundt; Tobias Weis; Kishore Konda; Visvanathan Ramesh (CoRR, 2017)

Rethinking Layer-wise Feature Amounts in Convolutional Neural Network Architectures. - Martin Mundt; Sagnik Majumder; Tobias Weis; Visvanathan Ramesh (CoRR, 2018)

Meta-Learning Convolutional Neural Architectures for Multi-Target Concrete Defect Classification With the COncrete DEfect BRidge IMage Dataset. - Martin Mundt; Sagnik Majumder; Sreenivas Murali; Panagiotis Panetsos; Visvanathan Ramesh (CVPR, 2019)

Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers? - Martin Mundt; Iuliia Pliushch; Sagnik Majumder; Visvanathan Ramesh (ICCV Workshops, 2019)

Exploring Pie Menus for System Control Tasks in Virtual Reality. - Martin Mundt; Tintu Mathew (MuC, 2019)

Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition. - Martin Mundt; Sagnik Majumder; Iuliia Pliushch; Visvanathan Ramesh (CoRR, 2019)

An Evaluation of Pie Menus for System Control in Virtual Reality. - Martin Mundt; Tintu Mathew (NordiCHI, 2020)

A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning. - Martin Mundt; Yong Won Hong; Iuliia Pliushch; Visvanathan Ramesh (CoRR, 2020)

Avalanche: An End-to-End Library for Continual Learning. - 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; et al. (CVPR Workshops, 2021)

Neural Architecture Search of Deep Priors: Towards Continual Learning Without Catastrophic Interference. - Martin Mundt; Iuliia Pliushch; Visvanathan Ramesh (CVPR Workshops, 2021)

A Procedural World Generation Framework for Systematic Evaluation of Continual Learning. - Timm Hess; Martin Mundt; Iuliia Pliushch; Visvanathan Ramesh (NeurIPS Datasets and Benchmarks, 2021)

Elevating Perceptual Sample Quality in PCs through Differentiable Sampling. - Steven Lang; Martin Mundt; Fabrizio Ventola; Robert Peharz; Kristian Kersting (Pre-Registration Workshop @ NeurIPS, 2021)

Avalanche: an End-to-End Library for Continual Learning. - 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; et al. (CoRR, 2021)

Designing deep neural networks for continual learning in an open world. - Martin Mundt (2021)

When Deep Classifiers Agree: Analyzing Correlations Between Learning Order and Image Statistics. - Iuliia Pliushch; Martin Mundt; Nicolas Lupp; Visvanathan Ramesh (ECCV (8), 2022)

CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability. - Martin Mundt; Steven Lang; Quentin Delfosse; Kristian Kersting (ICLR, 2022)

Predictive Whittle networks for time series. - Zhongjie Yu 001; Fabrizio Ventola; Nils Thoma; Devendra Singh Dhami; Martin Mundt; Kristian Kersting (UAI, 2022)

Optical see-through augmented reality can induce severe motion sickness. - Mara Kaufeld; Martin Mundt; Sarah Forst; Heiko Hecht (Displays, 2022)

Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition. - Martin Mundt; Iuliia Pliushch; Sagnik Majumder; Yong Won Hong; Visvanathan Ramesh (J. Imaging, 2022)

Return of the normal distribution: Flexible deep continual learning with variational auto-encoders. - Yongwon Hong; Martin Mundt; Sungho Park; Yungjung Uh; Hyeran Byun (Neural Networks, 2022)

Continual Causality: A Retrospective of the Inaugural AAAI-23 Bridge Program. - Martin Mundt; Keiland W. Cooper; Devendra Singh Dhami; Adèle H. Ribeiro; James Seale Smith; Alexis Bellot; Tyler L. Hayes (AAAI Bridge Program, 2023)

Benchmarking the Second Generation of Intel SGX for Machine Learning Workloads. - Adrian Lutsch; Gagandeep Singh; Martin Mundt; Ragnar Mogk; Carsten Binnig (BTW, 2023)

Queer In AI: A Case Study in Community-Led Participatory AI. - 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 009; et al. (FAccT, 2023)

Probabilistic circuits that know what they don't know. - Fabrizio Ventola; Steven Braun; Zhongjie Yu 001; Martin Mundt; Kristian Kersting (UAI, 2023)

Queer In AI: A Case Study in Community-Led Participatory AI. - Anaelia Ovalle; Arjun Subramonian; Ashwin Singh; Claas Voelcker; Danica J. Sutherland; Davide Locatelli; Eva Breznik; Filip Klubicka; Hang Yuan; Hetvi Jethwani; Huan Zhang 009; Jaidev Shriram; et al. (CoRR, 2023)

Self Expanding Neural Networks. - Rupert Mitchell; Martin Mundt; Kristian Kersting (CoRR, 2023)

A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning. - Martin Mundt; Yongwon Hong; Iuliia Pliushch; Visvanathan Ramesh (Neural Networks, 2023)

Deep Classifier Mimicry without Data Access. - Steven Braun; Martin Mundt; Kristian Kersting (AISTATS, 2024)

Masked Autoencoders are Efficient Continual Federated Learners. - Subarnaduti Paul; Lars-Joel Frey; Roshni Ramanna Kamath; Kristian Kersting; Martin Mundt (CoLLAs, 2024)

Adaptive Rational Activations to Boost Deep Reinforcement Learning. - Quentin Delfosse; Patrick Schramowski; Martin Mundt; Alejandro Molina 001; Kristian Kersting (ICLR, 2024)

Distribution-Aware Replay for Continual MRI Segmentation. - Nick Lemke; Camila González; Anirban Mukhopadhyay 003; Martin Mundt (AIPAD/PILM@MICCAI, 2024)

Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation. - Achref Jaziri; Martin Mundt; Andres Fernandez Rodriguez; Visvanathan Ramesh (WACV, 2024)

BOWLL: A Deceptively Simple Open World Lifelong Learner. - Roshni Kamath; Rupert Mitchell; Subarnaduti Paul; Kristian Kersting; Martin Mundt (CoRR, 2024)

Where is the Truth? The Risk of Getting Confounded in a Continual World. - Florian Peter Busch; Roshni Kamath; Rupert Mitchell; Wolfgang Stammer; Kristian Kersting; Martin Mundt (CoRR, 2024)

Core Tokensets for Data-efficient Sequential Training of Transformers. - Subarnaduti Paul; Manuel Brack; Patrick Schramowski; Kristian Kersting; Martin Mundt (CoRR, 2024)

Aligning Generalisation Between Humans and Machines. - Filip Ilievski; Barbara Hammer; Frank van Harmelen; Benjamin Paassen; Sascha Saralajew; Ute Schmid; Michael Biehl; Marianna Bolognesi; Xin Luna Dong; Kiril Gashteovski; Pascal Hitzler; Giuseppe Marra; et al. (CoRR, 2024)

Continual Learning: Applications and the Road Forward. - 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; et al. (Trans. Mach. Learn. Res., 2024)

Scaling Probabilistic Circuits via Data Partitioning. - Jonas Seng; Florian Peter Busch; Pooja Prasad; Devendra Singh Dhami; Martin Mundt; Kristian Kersting (UAI, 2025)

The Cake that is Intelligence and Who Gets to Bake it: An AI Analogy and its Implications for Participation. - Martin Mundt; Anaelia Ovalle; Felix Friedrich; Pranav Agrawal 003; Subarnaduti Paul; Manuel Brack; Kristian Kersting; William Agnew (CoRR, 2025)

Continual Learning Should Move Beyond Incremental Classification. - Rupert Mitchell; Antonio Alliegro; Raffaello Camoriano; Dustin Carrión-Ojeda; Antonio Carta; Georgia Chalvatzaki; Nikhil Churamani; Carlo D'Eramo; Samin Hamidi; Robin Hesse; Fabian Hinder; Roshni Kamath; et al. (CoRR, 2025)

Tractable Representation Learning with Probabilistic Circuits. - Steven Braun; Sahil Sidheekh; Antonio Vergari; Martin Mundt; Sriraam Natarajan; Kristian Kersting (CoRR, 2025)