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Weijie Zhao

Researcher at Baidu

Publications -  49
Citations -  1596

Weijie Zhao is an academic researcher from Baidu. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 13, co-authored 31 publications receiving 1137 citations. Previous affiliations of Weijie Zhao include University of Shanghai for Science and Technology & University of California, Merced.

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Journal ArticleDOI

Illuminating gravitational waves: A concordant picture of photons from a neutron star merger

Mansi M. Kasliwal, +94 more
- 22 Dec 2017 - 
TL;DR: It is demonstrated that merging neutron stars are a long-sought production site forging heavy elements by r-process nucleosynthesis, which is dissimilar to classical short gamma-ray bursts with ultrarelativistic jets.
Journal ArticleDOI

Illuminating Gravitational Waves: A Concordant Picture of Photons from a Neutron Star Merger

Mansi M. Kasliwal, +94 more
TL;DR: In this paper, the authors established the physical association of an electromagnetic counterpart EM170817 to gravitational waves (GW 170817) detected from merging neutron stars by synthesizing a panchromatic dataset.
Proceedings ArticleDOI

AIBox: CTR Prediction Model Training on a Single Node

TL;DR: AIBox is presented, a centralized system to train CTR models with tens-of-terabytes-scale parameters by employing solid-state drives (SSDs) and GPUs, and a bi-level cache management system over SSDs to store the 10TB parameters while providing low-latency accesses.
Posted Content

Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems

TL;DR: In this paper, a distributed GPU hierarchical parameter server for massive scale deep learning ad systems is proposed, which utilizes GPU High Bandwidth Memory, CPU main memory and SSD as 3-layer hierarchical storage.
Proceedings ArticleDOI

SONG: Approximate Nearest Neighbor Search on GPU

TL;DR: This paper presents a novel framework that decouples the searching on graph algorithm into 3 stages, in order to parallel the performance-crucial distance computation and proposes novel ANN-specific optimization methods that eliminate dynamic GPU memory allocations and trade computations for less GPU memory consumption.