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ORCIDThomas Unterthiner
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2020 – today
- 2024
[c16]Lukas Muttenthaler, Robert A. Vandermeulen, Qiuyi Zhang, Thomas Unterthiner, Klaus-Robert Müller:
Set Learning for Accurate and Calibrated Models. ICLR 2024- [i23]Lucas Beyer, Andreas Steiner, André Susano Pinto, Alexander Kolesnikov, Xiao Wang, Daniel Salz, Maxim Neumann, Ibrahim Alabdulmohsin, Michael Tschannen, Emanuele Bugliarello, Thomas Unterthiner
, Daniel Keysers, Skanda Koppula, Fangyu Liu, Adam Grycner, Alexey A. Gritsenko, Neil Houlsby, Manoj Kumar, Keran Rong, Julian Eisenschlos, Rishabh Kabra, Matthias Bauer, Matko Bosnjak, Xi Chen, Matthias Minderer, Paul Voigtlaender, Ioana Bica, Ivana Balazevic, Joan Puigcerver, Pinelopi Papalampidi, Olivier J. Hénaff, Xi Xiong, Radu Soricut, Jeremiah Harmsen, Xiaohua Zhai:
PaliGemma: A versatile 3B VLM for transfer. CoRR abs/2407.07726 (2024) - [i22]Lukas Muttenthaler, Klaus Greff, Frieda Born, Bernhard Spitzer, Simon Kornblith, Michael C. Mozer, Klaus-Robert Müller, Thomas Unterthiner, Andrew K. Lampinen:
Aligning Machine and Human Visual Representations across Abstraction Levels. CoRR abs/2409.06509 (2024) - 2023
- [i21]Lukas Muttenthaler, Robert A. Vandermeulen, Qiuyi Zhang, Thomas Unterthiner, Klaus-Robert Müller:
Set Learning for Accurate and Calibrated Models. CoRR abs/2307.02245 (2023) - [i20]Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller, Andi Peng, Andreea Bobu, Been Kim, Bradley C. Love, Erin Grant, Jascha Achterberg, Joshua B. Tenenbaum, Katherine M. Collins, Katherine L. Hermann, Kerem Oktar, Klaus Greff, Martin N. Hebart, Nori Jacoby, Qiuyi Zhang, Raja Marjieh, Robert Geirhos, Sherol Chen, Simon Kornblith, Sunayana Rane, Talia Konkle, Thomas P. O'Connell, Thomas Unterthiner, Andrew K. Lampinen, Klaus-Robert Müller, Mariya Toneva, Thomas L. Griffiths:
Getting aligned on representational alignment. CoRR abs/2310.13018 (2023) - 2022
- [c15]Utku Evci, Bart van Merrienboer, Thomas Unterthiner, Fabian Pedregosa, Max Vladymyrov:
GradMax: Growing Neural Networks using Gradient Information. ICLR 2022 - [i19]Utku Evci, Max Vladymyrov, Thomas Unterthiner, Bart van Merriënboer, Fabian Pedregosa:
GradMax: Growing Neural Networks using Gradient Information. CoRR abs/2201.05125 (2022) - [i18]Oliver T. Unke, Martin Stöhr, Stefan Ganscha, Thomas Unterthiner, Hartmut Maennel, Sergii Kashubin, Daniel Ahlin, Michael Gastegger, Leonardo Medrano Sandonas, Alexandre Tkatchenko, Klaus-Robert Müller:
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations. CoRR abs/2205.08306 (2022) - 2021
- [c14]Jean-Baptiste Cordonnier, Aravindh Mahendran, Alexey Dosovitskiy, Dirk Weissenborn, Jakob Uszkoreit, Thomas Unterthiner:
Differentiable Patch Selection for Image Recognition. CVPR 2021: 2351-2360 - [c13]Srinadh Bhojanapalli, Ayan Chakrabarti, Daniel Glasner, Daliang Li, Thomas Unterthiner, Andreas Veit:
Understanding Robustness of Transformers for Image Classification. ICCV 2021: 10211-10221 - [c12]Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby:
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR 2021 - [c11]Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, Alexey Dosovitskiy:
Do Vision Transformers See Like Convolutional Neural Networks? NeurIPS 2021: 12116-12128 - [c10]Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy:
MLP-Mixer: An all-MLP Architecture for Vision. NeurIPS 2021: 24261-24272 - [i17]Srinadh Bhojanapalli, Ayan Chakrabarti, Daniel Glasner, Daliang Li, Thomas Unterthiner, Andreas Veit:
Understanding Robustness of Transformers for Image Classification. CoRR abs/2103.14586 (2021) - [i16]Jean-Baptiste Cordonnier, Aravindh Mahendran, Alexey Dosovitskiy, Dirk Weissenborn, Jakob Uszkoreit, Thomas Unterthiner:
Differentiable Patch Selection for Image Recognition. CoRR abs/2104.03059 (2021) - [i15]Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy:
MLP-Mixer: An all-MLP Architecture for Vision. CoRR abs/2105.01601 (2021) - [i14]Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, Alexey Dosovitskiy:
Do Vision Transformers See Like Convolutional Neural Networks? CoRR abs/2108.08810 (2021) - 2020
- [c9]Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, Thomas Kipf:
Object-Centric Learning with Slot Attention. NeurIPS 2020 - [i13]Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, Ilya O. Tolstikhin:
Predicting Neural Network Accuracy from Weights. CoRR abs/2002.11448 (2020) - [i12]Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, Thomas Kipf:
Object-Centric Learning with Slot Attention. CoRR abs/2006.15055 (2020) - [i11]Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby:
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. CoRR abs/2010.11929 (2020)
2010 – 2019
- 2019
- [c8]Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, Sylvain Gelly:
FVD: A new Metric for Video Generation. DGS@ICLR 2019 - [c7]Jose A. Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, Sepp Hochreiter:
RUDDER: Return Decomposition for Delayed Rewards. NeurIPS 2019: 13544-13555 - [p2]Markus Hofmarcher, Thomas Unterthiner, Jose A. Arjona-Medina, Günter Klambauer, Sepp Hochreiter, Bernhard Nessler:
Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation. Explainable AI 2019: 285-296 - [p1]Kristina Preuer, Günter Klambauer, Friedrich Rippmann, Sepp Hochreiter, Thomas Unterthiner:
Interpretable Deep Learning in Drug Discovery. Explainable AI 2019: 331-345 - [i10]Kristina Preuer, Günter Klambauer, Friedrich Rippmann, Sepp Hochreiter, Thomas Unterthiner:
Interpretable Deep Learning in Drug Discovery. CoRR abs/1903.02788 (2019) - 2018
- [j4]Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, Günter Klambauer:
Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery. J. Chem. Inf. Model. 58(9): 1736-1741 (2018) - [c6]Thomas Unterthiner, Bernhard Nessler, Calvin Seward, Günter Klambauer, Martin Heusel, Hubert Ramsauer, Sepp Hochreiter:
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields. ICLR (Poster) 2018 - [c5]Calvin Seward, Thomas Unterthiner, Urs Bergmann, Nikolay Jetchev, Sepp Hochreiter:
First Order Generative Adversarial Networks. ICML 2018: 4574-4583 - [i9]Calvin Seward, Thomas Unterthiner, Urs Bergmann, Nikolay Jetchev, Sepp Hochreiter:
First Order Generative Adversarial Networks. CoRR abs/1802.04591 (2018) - [i8]Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, Günter Klambauer:
Fréchet ChemblNet Distance: A metric for generative models for molecules. CoRR abs/1803.09518 (2018) - [i7]Jose A. Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Sepp Hochreiter:
RUDDER: Return Decomposition for Delayed Rewards. CoRR abs/1806.07857 (2018) - [i6]Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, Sylvain Gelly:
Towards Accurate Generative Models of Video: A New Metric & Challenges. CoRR abs/1812.01717 (2018) - 2017
- [j3]Djork-Arné Clevert, Thomas Unterthiner, Gundula Povysil, Sepp Hochreiter:
Rectified factor networks for biclustering of omics data. Bioinform. 33(14): i59-i66 (2017) - [c4]Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter:
Self-Normalizing Neural Networks. NIPS 2017: 971-980 - [c3]Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Sepp Hochreiter:
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. NIPS 2017: 6626-6637 - [i5]Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter:
Self-Normalizing Neural Networks. CoRR abs/1706.02515 (2017) - [i4]Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Günter Klambauer, Sepp Hochreiter:
GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium. CoRR abs/1706.08500 (2017) - [i3]Thomas Unterthiner, Bernhard Nessler, Günter Klambauer, Martin Heusel, Hubert Ramsauer, Sepp Hochreiter:
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields. CoRR abs/1708.08819 (2017) - 2016
- [c2]Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter:
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). ICLR (Poster) 2016 - 2015
- [j2]Günter Klambauer, Martin Wischenbart, Michael Mahr, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter:
Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map. Bioinform. 31(20): 3392-3394 (2015) - [c1]Djork-Arné Clevert, Andreas Mayr, Thomas Unterthiner, Sepp Hochreiter:
Rectified Factor Networks. NIPS 2015: 1855-1863 - [i2]Djork-Arné Clevert, Thomas Unterthiner, Andreas Mayr, Hubert Ramsauer, Sepp Hochreiter:
Rectified Factor Networks. CoRR abs/1502.06464 (2015) - [i1]Thomas Unterthiner, Andreas Mayr, Günter Klambauer, Sepp Hochreiter:
Toxicity Prediction using Deep Learning. CoRR abs/1503.01445 (2015) - 2011
- [j1]Thomas Unterthiner, Anne-Kathrin Schultz, Jan Bulla, Burkhard Morgenstern, Mario Stanke, Ingo Bulla:
Detection of viral sequence fragments of HIV-1 subfamilies yet unknown. BMC Bioinform. 12: 93 (2011)
Coauthor Index
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last updated on 2024-10-15 00:25 CEST by the dblp team
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