Daniel Keysers
Studied Computer Science in Aachen, Germany and Madrid, Spain; PhD in Computer Science (Image Understanding, Pattern Recognition), RWTH Aachen, Germany; PostDoc at German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany; Joined Google Zurich in 2007 as Software Engineer; projects at Google: YouTube Content-ID; Handwriting Recognition; Natural Language Understanding; Deep Learning & Computer Vision Research.
Research Areas
Authored Publications
Sort By
Google
PaLI-X: On Scaling up a Multilingual Vision and Language Model
Josip Djolonga
Piotr Padlewski
Basil Mustafa
Carlos Riquelme
Sebastian Goodman
Yi Tay
Siamak Shakeri
Daniel Salz
Michael Tschannen
Hexiang (Frank) Hu
Mandar Joshi
Matthias Minderer
Filip Pavetić
Gang Li
Lucas Beyer
Anurag Arnab
Yuanzhong Xu
Keran Rong
Alexander Kolesnikov
Xiaohua Zhai
Neil Houlsby
Computer Vision and Pattern Recognition Conference (CVPR) (2024)
Scaling Vision Transformers to 22 Billion Parameters
Josip Djolonga
Basil Mustafa
Piotr Padlewski
Justin Gilmer
Mathilde Caron
Rodolphe Jenatton
Lucas Beyer
Michael Tschannen
Anurag Arnab
Carlos Riquelme
Matthias Minderer
Gamaleldin Elsayed
Fisher Yu
Avital Oliver
Fantine Huot
Mark Collier
Vighnesh Birodkar
Yi Tay
Alexander Kolesnikov
Filip Pavetić
Thomas Kipf
Xiaohua Zhai
Neil Houlsby
Arxiv (2023)
MLP-Mixer: An All-MLP Architecture for Vision
Neil Houlsby
Alexander Kolesnikov
Lucas Beyer
Xiaohua Zhai
Thomas Unterthiner
Jessica Yung
Jakob Uszkoreit
Alexey Dosovitskiy
NeurIPS 2021 (poster)
Scalable Transfer Learning with Expert Models
Carlos Riquelme
Basil Mustafa
Cedric Renggli
André Susano Pinto
Sylvain Gelly
Neil Houlsby
ICLR 2021 (2021)
Continental-scale building detection from high resolution satellite imagery
Wojciech Sirko
Yasser Salah Eddine Bouchareb
Maxim Neumann
Moustapha Cisse
arXiv (2021)
Scaling Vision with Sparse Mixture of Experts
Carlos Riquelme
Basil Mustafa
Maxim Neumann
Rodolphe Jenatton
André Susano Pinto
Neil Houlsby
Neurips 2021. (2021)
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
Nathanael Schärli
Nathan Scales
Hylke Buisman
Daniel Furrer
Nikola Momchev
Danila Sinopalnikov
Lukasz Stafiniak
Tibor Tihon
Dmitry Tsarkov
Marc van Zee
ICLR (2020)
What Do Neural Networks Learn When Trained With Random Labels?
Hartmut Maennel
Robert J. N. Baldock
Sylvain Gelly
NeurIPS 2020
Fast Multi-language LSTM-based Online Handwriting Recognition
Thomas Deselaers
Alexander Daryin
Marcos Calvo
Li-Lun Wang
Sandro Feuz
Philippe Gervais
International Journal on Document Analysis and Recognition (IJDAR) (2020)
Multi-Language Online Handwriting Recognition
Thomas Deselaers
Li-Lun Wang
IEEE Transactions on Pattern Analysis and Machine Intelligence (2016)
