Lukas Schott - RankMe

25 papers A* 4Misc 1Journal 15Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Investigating Uncertainty Weighting for Multi-Task Learning: Insights and Analytical Alternative. Int. J. Comput. Vis. Lukas Kirchdorfer, Tobias Sesterhenn, Christian Bartelt, Heiner Stuckenschmidt, Lukas Schott, Jan Mathias Köhler
2025	J	`jnl`	Improving Knowledge Distillation Under Unknown Covariate Shift Through Confidence-Guided Data Augmentation. CoRR Niclas Popp, Kevin Alexander Laube, Matthias Hein, Lukas Schott
2024	J	`jnl`	Analytical Uncertainty-Based Loss Weighting in Multi-Task Learning. CoRR Lukas Kirchdorfer, Cathrin Elich, Simon Kutsche, Heiner Stuckenschmidt, Lukas Schott, Jan Mathias Köhler
2024	—	`conf`	Analytical Uncertainty-Based Loss Weighting in Multi-task Learning. GCPR (1) Lukas Kirchdorfer, Cathrin Elich, Simon Kutsche, Heiner Stuckenschmidt, Lukas Schott, Jan Mathias Köhler
2024	J	`jnl`	Attention Is All You Need For Mixture-of-Depths Routing. CoRR Advait Gadhikar, Souptik Kumar Majumdar, Niclas Popp, Piyapat Saranrittichai, Martin Rapp, Lukas Schott
2024	—	`conf`	Examining Common Paradigms in Multi-task Learning. GCPR (1) Cathrin Elich, Lukas Kirchdorfer, Jan Mathias Köhler, Lukas Schott
2024	J	`jnl`	Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data. CoRR Leonhard Hennicke, Christian Medeiros Adriano, Holger Giese, Jan Mathias Köhler, Lukas Schott
2024	J	`jnl`	Object-Focused Data Selection for Dense Prediction Tasks. CoRR Niclas Popp, Dan Zhang, Jan Hendrik Metzen, Matthias Hein, Lukas Schott
2023	J	`jnl`	Challenging Common Assumptions in Multi-task Learning. CoRR Cathrin Elich, Lukas Kirchdorfer, Jan Mathias Köhler, Lukas Schott
2023	A*	`conf`	Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles. ICLR Martin Bjerke, Lukas Schott, Kristopher T. Jensen, Claudia Battistin, David A. Klindt, Benjamin Adric Dunn
2022	J	`jnl`	Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles. CoRR Martin Bjerke, Lukas Schott, Kristopher T. Jensen, Claudia Battistin, David A. Klindt, Benjamin A. Dunn
2022	A*	`conf`	Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. ICLR Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Vincent Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel
2021	J	`jnl`	Score-Based Generative Classifiers. CoRR Roland S. Zimmermann, Lukas Schott, Yang Song, Benjamin A. Dunn, David A. Klindt
2021	—	`—`	Selected Inductive Biases in Neural Networks To Generalize Beyond the Training Domain. Lukas Schott
2021	A*	`conf`	Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding. ICLR David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan M. Paiton
2021	J	`jnl`	Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. CoRR Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter V. Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel
2020	—	`conf`	A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions. ECCV (3) Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, Wieland Brendel
2020	J	`jnl`	Increasing the robustness of DNNs against image corruptions by playing the Game of Noise. CoRR Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, Wieland Brendel
2020	J	`jnl`	Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding. CoRR David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan M. Paiton
2019	—	`conf`	Towards the first adversarially robust neural network model on MNIST. ICLR (Poster) Lukas Schott, Jonas Rauber, Matthias Bethge, Wieland Brendel
2018	J	`jnl`	Robust Perception through Analysis by Synthesis. CoRR Lukas Schott, Jonas Rauber, Wieland Brendel, Matthias Bethge
2017	Misc	`conf`	Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction. ICASSP Shengdong Zhang, Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, Mohak Shah
2017	A*	`conf`	Learned Watershed: End-to-End Learning of Seeded Segmentation. ICCV Steffen Wolf, Lukas Schott, Ullrich Köthe, Fred A. Hamprecht
2017	J	`jnl`	Learned Watershed: End-to-End Learning of Seeded Segmentation. CoRR Steffen Wolf, Lukas Schott, Ullrich Köthe, Fred A. Hamprecht
2015	J	`jnl`	Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning. CoRR Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, Mohak Shah