J
Jakob Uszkoreit
Researcher at Google
Publications - 85
Citations - 83076
Jakob Uszkoreit is an academic researcher from Google. The author has contributed to research in topics: Machine translation & Transformer (machine learning model). The author has an hindex of 36, co-authored 84 publications receiving 37432 citations. Previous affiliations of Jakob Uszkoreit include University of California, Berkeley.
Papers
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Proceedings Article
Object-Centric Learning with Slot Attention
Francesco Locatello,Dirk Weissenborn,Thomas Unterthiner,Aravindh Mahendran,Georg Heigold,Jakob Uszkoreit,Alexey Dosovitskiy,Thomas Kipf +7 more
TL;DR: An architectural component that interfaces with perceptual representations such as the output of a convolutional neural network and produces a set of task-dependent abstract representations which are exchangeable and can bind to any object in the input by specializing through a competitive procedure over multiple rounds of attention is presented.
Proceedings Article
Tensor2Tensor for Neural Machine Translation
Ashish Vaswani,Samy Bengio,Eugene Brevdo,François Chollet,Aidan N. Gomez,Stephan Gouws,Llion Jones,Łukasz Kaiser,Nal Kalchbrenner,Niki Parmar,Ryan Sepassi,Noam Shazeer,Jakob Uszkoreit +12 more
TL;DR: Tensor2Tensor as mentioned in this paper is a library for deep learning models that is well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model.
Proceedings Article
Music Transformer: Generating Music with Long-Term Structure
Cheng-Zhi Anna Huang,Ashish Vaswani,Jakob Uszkoreit,Noam Shazeer,Ian Simon,Curtis Hawthorne,Andrew M. Dai,Matthew W. Hoffman,Monica Dinculescu,Douglas Eck +9 more
TL;DR: It is demonstrated that a Transformer with the modified relative attention mechanism can generate minutelong compositions with compelling structure, generate continuations that coherently elaborate on a given motif, and in a seq2seq setup generate accompaniments conditioned on melodies.
Posted Content
One Model To Learn Them All
Lukasz Kaiser,Aidan N. Gomez,Noam Shazeer,Ashish Vaswani,Niki Parmar,Llion Jones,Jakob Uszkoreit +6 more
TL;DR: It is shown that tasks with less data benefit largely from joint training with other tasks, while performance on large tasks degrades only slightly if at all, and that adding a block to the model never hurts performance and in most cases improves it on all tasks.
Posted Content
A Decomposable Attention Model for Natural Language Inference
TL;DR: This work proposes a simple neural architecture for natural language inference that uses attention to decompose the problem into subproblems that can be solved separately, thus making it trivially parallelizable.