activity
20202022
most citedMerlin HugeCTR: GPU-accelerated Recommender System Training and Inference

31 citations · 62 across the 8 of their papers we have counts for

collaborators

9 papers

cs.SD20221 cited

The ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge (ICSRC): Dataset, Tracks, Baseline and Results

Ao Zhang, Fan Yu, Kaixun Huang +7

This paper summarizes the outcomes from the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge (ICSRC). We first address the necessity of the challenge and then introduce…

cs.SD2022

The NPU-ASLP System for The ISCSLP 2022 Magichub Code-Swiching ASR Challenge

Yuhao Liang, Peikun Chen, Fan Yu +3

This paper describes our NPU-ASLP system submitted to the ISCSLP 2022 Magichub Code-Switching ASR Challenge. In this challenge, we first explore several popular end-to-end ASR arch…

cs.IR202221 cited

A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models

Yingcan Wei, Matthias Langer, Fan Yu +4

Recommendation systems are of crucial importance for a variety of modern apps and web services, such as news feeds, social networks, e-commerce, search, etc. To achieve peak predic…

cs.DC202231 cited

Merlin HugeCTR: GPU-accelerated Recommender System Training and Inference

Joey Wang, Yingcan Wei, Minseok Lee +9

In this talk, we introduce Merlin HugeCTR. Merlin HugeCTR is an open source, GPU-accelerated integration framework for click-through rate estimation. It optimizes both training and…

math.NA2021

BDF SAV schemes for time-fractional Allen-Cahn dissipative systems

Fan Yu, Minghua Chen

Recently, the error analysis of BDF SAV (scalar auxiliary variable) schemes are given in \cite{Huangg:20} for the classical Allen-Cahn equation. Howev…

cs.LG2021

AsymptoticNG: A regularized natural gradient optimization algorithm with look-ahead strategy

Zedong Tang, Fenlong Jiang, Junke Song +5

Optimizers that further adjust the scale of gradient, such as Adam, Natural Gradient (NG), etc., despite widely concerned and used by the community, are often found poor generaliza…