activity
20182022
most citedLingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

184 citations · 254 across the 4 of their papers we have counts for

collaborators

7 papers

cs.IR202219 cited

Monolith: Real Time Recommendation System With Collisionless Embedding Table

Zhuoran Liu, Leqi Zou, Xuan Zou +8

Building a scalable and real-time recommendation system is vital for many businesses driven by time-sensitive customer feedback, such as short-videos ranking or online ads. Despite…

cs.HC20212 cited

Toward Annotator Group Bias in Crowdsourcing

Haochen Liu, Joseph Thekinen, Sinem Mollaoglu +5

Crowdsourcing has emerged as a popular approach for collecting annotated data to train supervised machine learning models. However, annotator bias can lead to defective annotations…

cs.LG202049 cited

Talking-Heads Attention

Noam Shazeer, Zhenzhong Lan, Youlong Cheng +2

We introduce "talking-heads attention" - a variation on multi-head attention which includes linearprojections across the attention-heads dimension, immediately before and after the…

eess.IV2019

High Resolution Medical Image Analysis with Spatial Partitioning

Le Hou, Youlong Cheng, Noam Shazeer +6

Medical images such as 3D computerized tomography (CT) scans and pathology images, have hundreds of millions or billions of voxels/pixels. It is infeasible to train CNN models dire…

cs.LG2019184 cited

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

Jonathan Shen, Patrick Nguyen, Yonghui Wu +88

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…

cs.LG2018

Image Classification at Supercomputer Scale

Chris Ying, Sameer Kumar, Dehao Chen +2

Deep learning is extremely computationally intensive, and hardware vendors have responded by building faster accelerators in large clusters. Training deep learning models at petaFL…