184 citations · 432 across the 9 of their papers we have counts for
9 papers
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…
CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPU
Zangwei Zheng, Pengtai Xu, Xuan Zou +12
The click-through rate (CTR) prediction task is to predict whether a user will click on the recommended item. As mind-boggling amounts of data are produced online daily, accelerati…
Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers
Lin Guan, Xia Xiao, Ming Chen +1
Feature selection has been an essential step in developing industry-scale deep Click-Through Rate (CTR) prediction systems. The goal of neural feature selection (NFS) is to choose…
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…
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…
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…
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…
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…
Mesh-TensorFlow: Deep Learning for Supercomputers
Noam Shazeer, Youlong Cheng, Niki Parmar +9
Batch-splitting (data-parallelism) is the dominant distributed Deep Neural Network (DNN) training strategy, due to its universal applicability and its amenability to Single-Program…