Unterthiner, Thomas

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 47,472

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017) · arXiv

Generative Adversarial Networks and Image SynthesisStochastic Gradient Optimization Techniques

Cited by 8,365

Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015) · arXiv

Neural Networks and ApplicationsAdvanced Neural Network Applications

Cited by 4,513

Self-Normalizing Neural Networks

Klambauer, G., Unterthiner, T., Mayr, A., & Hochreiter, S. (2017) · arXiv

Neural Networks and ApplicationsMachine Learning and ELM

Cited by 2,267

MLP-Mixer: An all-MLP Architecture for Vision

Tolstikhin, I., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., et al. (2021) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 2,087

DeepTox: toxicity prediction using deep learning

Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 1,081

Do Vision Transformers See Like Convolutional Neural Networks?

Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., & Dosovitskiy, A. (2021) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 663

Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., Ceulemans, H., Clevert, D.-A., & Hochreiter, S. (2018) · Chemical Science

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 584

Understanding Robustness of Transformers for Image Classification

Bhojanapalli, S., Chakrabarti, A., Glasner, D., Li, D., Unterthiner, T., & Veit, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Adversarial Robustness in Machine LearningAdvanced Neural Network Applications

Cited by 440

Towards Accurate Generative Models of Video: A New Metric & Challenges

Unterthiner, T., van Steenkiste, S., Kurach, K., Marinier, R., Michalski, M., & Gelly, S. (2018) · arXiv

Generative Adversarial Networks and Image SynthesisArtificial Intelligence in Games

Cited by 204

Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation

Hofmarcher, M., Unterthiner, T., Arjona-Medina, J., Klambauer, G., Hochreiter, S., & Nessler, B. (2019) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety

Cited by 51

Getting aligned on representational alignment

Sucholutsky, I., Muttenthaler, L., Weller, A., Peng, A., Bobu, A., Kim, B., Love, B. C., Cueva, C. J., Grant, E., Groen, I., et al. (2023) · arXiv

Domain Adaptation and Few-Shot LearningExplainable Artificial Intelligence (XAI)

Cited by 36

PaliGemma: A versatile 3B VLM for transfer

Beyer, L., Steiner, A., Pinto, A. S., Kolesnikov, A., Wang, X., Salz, D., Neumann, M., Alabdulmohsin, I., Tschannen, M., Bugliarello, E., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 17