Daniel Duckworth
Daniel Duckworth received his Master of Science in Engineering from University of California, Berkeley under the supervision of Prof. Stuart J. Russell, where he worked on stochastic methods for Bayesian inference. Since joining Google Brain in 2017, Daniel has since branched off to numerical optimization, generalization, generative modeling, and natural language processing. Google Scholar Profile
Research Areas
Authored Publications
Sort By
Google
RUST: Latent Neural Scene Representations from Unposed Imagery
Thomas Kipf
Klaus Greff
Conference on Computer Vision and Pattern Recognition (CVPR) (2023) (to appear)
Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations
Henning Meyer
Urs Bergmann
Klaus Greff
Noha Radwan
Alexey Dosovitskiy
Jakob Uszkoreit
Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
Kubric: A scalable dataset generator
Anissa Yuenming Mak
Austin Stone
Carl Doersch
Cengiz Oztireli
Charles Herrmann
Daniel Rebain
Derek Nowrouzezahrai
Dmitry Lagun
Fangcheng Zhong
Florian Golemo
Francois Belletti
Henning Meyer
Hsueh-Ti (Derek) Liu
Issam Laradji
Klaus Greff
Kwang Moo Yi
Lucas Beyer
Matan Sela
Noha Radwan
Thomas Kipf
Tianhao Wu
Vincent Sitzmann
Yilun Du
Yishu Miao
(2022)
Object Scene Representation Transformer
Filip Pavetić
Leonidas Guibas
Klaus Greff
Thomas Kipf
Advances in Neural Information Processing Systems (2022), pp. 9512-9524
NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes
Noha Radwan*
Klaus Greff
Henning Meyer
Kyle Genova
Transactions on Machine Learning Research (2022)
NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
Ricardo Martin-Brualla*
Noha Radwan*
Alexey Dosovitskiy
Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
Whitening and second order optimization both destroy information about the dataset, and can make generalization impossible
Ethan S Dyer
Jascha Sohl-dickstein
Neha Wadia
Sam S. Schoenholz
ICML Spotlight (2021) (to appear)
Automatic Detection of Generated Text is Easiest when Humans are Fooled
Chris Callison-Burch
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020), pp. 1808-1822
Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset
Chinnadhurai Sankar
Arvind Neelakantan
Semih Yavuz
Ben Goodrich
Amit Dubey
Kyu-Young Kim
Andy Cedilnik
EMNLP (2019) (to appear)
