Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning. - Soheil Bahrampour; Naveen Ramakrishnan; Lukas Schott; Mohak Shah (CoRR, 2015)

Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction. - Shengdong Zhang; Soheil Bahrampour; Naveen Ramakrishnan; Lukas Schott; Mohak Shah (ICASSP, 2017)

Learned Watershed: End-to-End Learning of Seeded Segmentation. - Steffen Wolf 001; Lukas Schott; Ullrich Köthe; Fred A. Hamprecht (ICCV, 2017)

Robust Perception through Analysis by Synthesis. - Lukas Schott; Jonas Rauber; Wieland Brendel; Matthias Bethge (CoRR, 2018)

Towards the first adversarially robust neural network model on MNIST. - Lukas Schott; Jonas Rauber; Matthias Bethge; Wieland Brendel (ICLR (Poster), 2019)

A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions. - Evgenia Rusak; Lukas Schott; Roland S. Zimmermann; Julian Bitterwolf; Oliver Bringmann 001; Matthias Bethge; Wieland Brendel (ECCV (3), 2020)

Increasing the robustness of DNNs against image corruptions by playing the Game of Noise. - Evgenia Rusak; Lukas Schott; Roland S. Zimmermann; Julian Bitterwolf; Oliver Bringmann 001; Matthias Bethge; Wieland Brendel (CoRR, 2020)

Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding. - David A. Klindt; Lukas Schott; Yash Sharma 001; Ivan Ustyuzhaninov; Wieland Brendel; Matthias Bethge; Dylan M. Paiton (ICLR, 2021)

Score-Based Generative Classifiers. - Roland S. Zimmermann; Lukas Schott; Yang Song; Benjamin A. Dunn; David A. Klindt (CoRR, 2021)

Selected Inductive Biases in Neural Networks To Generalize Beyond the Training Domain. - Lukas Schott (2021)

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. - Lukas Schott; Julius von Kügelgen; Frederik Träuble; Peter Vincent Gehler; Chris Russell 001; Matthias Bethge; Bernhard Schölkopf; Francesco Locatello; Wieland Brendel (ICLR, 2022)

Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles. - Martin Bjerke; Lukas Schott; Kristopher T. Jensen; Claudia Battistin; David A. Klindt; Benjamin Adric Dunn (ICLR, 2023)

Challenging Common Assumptions in Multi-task Learning. - Cathrin Elich; Lukas Kirchdorfer; Jan Mathias Köhler; Lukas Schott (CoRR, 2023)

Examining Common Paradigms in Multi-task Learning. - Cathrin Elich; Lukas Kirchdorfer; Jan Mathias Köhler; Lukas Schott (GCPR (1), 2024)

Analytical Uncertainty-Based Loss Weighting in Multi-task Learning. - Lukas Kirchdorfer; Cathrin Elich; Simon Kutsche; Heiner Stuckenschmidt; Lukas Schott; Jan Mathias Köhler (GCPR (1), 2024)

Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data. - Leonhard Hennicke; Christian Medeiros Adriano; Holger Giese; Jan Mathias Köhler; Lukas Schott (CoRR, 2024)

Object-Focused Data Selection for Dense Prediction Tasks. - Niclas Popp; Dan Zhang; Jan Hendrik Metzen; Matthias Hein 001; Lukas Schott (CoRR, 2024)

Attention Is All You Need For Mixture-of-Depths Routing. - Advait Gadhikar; Souptik Kumar Majumdar; Niclas Popp; Piyapat Saranrittichai; Martin Rapp; Lukas Schott (CoRR, 2024)

Improving Knowledge Distillation Under Unknown Covariate Shift Through Confidence-Guided Data Augmentation. - Niclas Popp; Kevin Alexander Laube; Matthias Hein 001; Lukas Schott (CoRR, 2025)