JU
Jakob Uszkoreit
cs.LGcs.CLcs.CVstat.MLcs.AIcs.GRcs.ROcs.SDeess.AS
On Valency
published · living versionsW_qeugbgvj·v1 · currentpublished
Preprints & journals
25 papers in the corpus · 2006–2021Attention Is All You Need1706.03762v7 · Ashish Vaswani, Noam Shazeer, Niki Parmar et al.2017 · 26,892 citationsarXivon Valency
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers2106.10270v2 · Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai et al.2021 · 271 citationsTransactions on Machine Learning Research (05/2022)
Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations2111.13152v3 · Mehdi S. M. Sajjadi, Henning Meyer, Etienne Pot et al.2021 · 115 citationsCVPR 2022
MLP-Mixer: An all-MLP Architecture for Vision2105.01601v4 · Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov et al.2021 · 1,448 citationsarXiv
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale2010.11929v2 · Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov et al.2020 · 22,512 citationsarXiv
Differentiable Patch Selection for Image Recognition2104.03059v1 · Jean-Baptiste Cordonnier, Aravindh Mahendran, Alexey Dosovitskiy et al.2021 · 105 citationsarXiv
Towards End-to-End In-Image Neural Machine Translation2010.10648v1 · Elman Mansimov, Mitchell Stern, Mia Chen et al.2020 · 18 citationsarXiv
Object-Centric Learning with Slot Attention2006.15055v2 · Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner et al.2020 · 288 citationsarXiv
Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals.32873773 · Popel, Martin, Tomkova, Marketa, Tomek, Jakub et al.2020 · 308 citationsNature communications. 2020;11(1):4381
Scaling Autoregressive Video Models1906.02634v3 · Dirk Weissenborn, Oscar Tackstrom, Jakob Uszkoreit2019 · 14 citationsarXiv
An Empirical Study of Generation Order for Machine Translation1910.13437v1 · William Chan, Mitchell Stern, Jamie Kiros et al.2019 · 8 citationsarXiv
KERMIT: Generative Insertion-Based Modeling for Sequences1906.01604v1 · William Chan, Nikita Kitaev, Kelvin Guu et al.2019 · 65 citationsarXiv
Universal Transformers1807.03819v3 · Mostafa Dehghani, Stephan Gouws, Oriol Vinyals et al.2018 · 382 citationsarXiv
Insertion Transformer: Flexible Sequence Generation via Insertion Operations1902.03249v1 · Mitchell Stern, William Chan, Jamie Kiros et al.2019 · 217 citationsarXiv
Music Transformer1809.04281v3 · Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit et al.2018 · 73 citationsarXiv
Blockwise Parallel Decoding for Deep Autoregressive Models1811.03115v1 · Mitchell Stern, Noam Shazeer, Jakob Uszkoreit2018 · 7 citationsarXiv
Image Transformer1802.05751v3 · Niki Parmar, Ashish Vaswani, Jakob Uszkoreit et al.2018 · 227 citationsarXiv
Fast Decoding in Sequence Models using Discrete Latent Variables1803.03382v6 · \Lukasz Kaiser, Aurko Roy, Ashish Vaswani et al.2018 · 169 citationsarXiv
Self-Attention with Relative Position Representations1803.02155v2 · Peter Shaw, Jakob Uszkoreit, Ashish Vaswani2018 · 2,526 citationsarXiv
Tensor2Tensor for Neural Machine Translation1803.07416v1 · Ashish Vaswani, Samy Bengio, Eugene Brevdo et al.2018 · 14 citationsarXiv
Neural Paraphrase Identification of Questions with Noisy Pretraining1704.04565v2 · Gaurav Singh Tomar, Thyago Duque, Oscar Tackstrom et al.2017 · 72 citationsarXiv
One Model To Learn Them All1706.05137v1 · Lukasz Kaiser, Aidan N. Gomez, Noam Shazeer et al.2017 · 255 citationsarXiv
Hierarchical Question Answering for Long Documents1611.01839v2 · Eunsol Choi, Daniel Hewlett, Alexandre Lacoste et al.2016 · 8 citationsarXiv
A Decomposable Attention Model for Natural Language Inference1606.01933v2 · Ankur P. Parikh, Oscar Tackstrom, Dipanjan Das et al.2016 · 1,444 citationsarXiv
Automated nuclear segmentation in the determination of the Ki-67 labeling index in meningiomas.16550739 · Kim, Y J, Romeike, B F M, Uszkoreit, J et al.2006 · 52 citationsClinical neuropathology. 2006;25(2):67-73
Career total: 49 works. 25 are in this corpus.
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