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Niki Parmar
Researcher at Google
Publications - 41
Citations - 66115
Niki Parmar is an academic researcher from Google. The author has contributed to research in topics: Transformer (machine learning model) & Machine translation. The author has an hindex of 22, co-authored 39 publications receiving 31763 citations. Previous affiliations of Niki Parmar include University of Southern California.
Papers
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Proceedings ArticleDOI
Conformer: Convolution-augmented Transformer for Speech Recognition
Anmol Gulati,James Qin,Chung-Cheng Chiu,Niki Parmar,Yu Zhang,Jiahui Yu,Wei Han,Shibo Wang,Zhengdong Zhang,Yonghui Wu,Ruoming Pang +10 more
TL;DR: Conformer as mentioned in this paper combines convolution neural networks and transformers to model both local and global dependencies of an audio sequence in a parameter-efficient way, achieving state-of-the-art accuracies.
Proceedings Article
Stand-Alone Self-Attention in Vision Models
TL;DR: The results establish that stand-alone self-attention is an important addition to the vision practitioner's toolbox and is especially impactful when used in later layers.
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.
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.
Proceedings ArticleDOI
Scaling Local Self-Attention for Parameter Efficient Visual Backbones
Ashish Vaswani,Prajit Ramachandran,Aravind Srinivas,Niki Parmar,Blake A. Hechtman,Jonathon Shlens +5 more
TL;DR: In this article, self-attention has been shown to have encouraging improvements on accuracy-parameter trade-offs compared to baseline convolutional models such as ResNet-50.