Search Results for author: Liang Xiong

Found 10 papers, 4 papers with code

Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale

no code implementations14 Nov 2023 Wei Wen, Kuang-Hung Liu, Igor Fedorov, Xin Zhang, Hang Yin, Weiwei Chu, Kaveh Hassani, Mengying Sun, Jiang Liu, Xu Wang, Lin Jiang, Yuxin Chen, Buyun Zhang, Xi Liu, Dehua Cheng, Zhengxing Chen, Guang Zhao, Fangqiu Han, Jiyan Yang, Yuchen Hao, Liang Xiong, Wen-Yen Chen

In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle.

Neural Architecture Search

NASRec: Weight Sharing Neural Architecture Search for Recommender Systems

2 code implementations14 Jul 2022 Tunhou Zhang, Dehua Cheng, Yuchen He, Zhengxing Chen, Xiaoliang Dai, Liang Xiong, Feng Yan, Hai Li, Yiran Chen, Wei Wen

To overcome the data multi-modality and architecture heterogeneity challenges in the recommendation domain, NASRec establishes a large supernet (i. e., search space) to search the full architectures.

Click-Through Rate Prediction Neural Architecture Search +1

Learning Two-Time-Scale Representations For Large Scale Recommendations

no code implementations1 Jan 2021 Xinshi Chen, Yan Zhu, Haowen Xu, Muhan Zhang, Liang Xiong, Le Song

We propose a surprisingly simple but effective two-time-scale (2TS) model for learning user representations for recommendation.

Vocal Bursts Valence Prediction

Kernels on Sample Sets via Nonparametric Divergence Estimates

no code implementations1 Feb 2012 Danica J. Sutherland, Liang Xiong, Barnabás Póczos, Jeff Schneider

Most machine learning algorithms, such as classification or regression, treat the individual data point as the object of interest.

Anomaly Detection BIG-bench Machine Learning +2

Group Anomaly Detection using Flexible Genre Models

no code implementations NeurIPS 2011 Liang Xiong, Barnabás Póczos, Jeff G. Schneider

We evaluate the effectiveness of FGM on both synthetic and real data sets including images and turbulence data, and show that it is superior to existing approaches in detecting group anomalies.

Group Anomaly Detection

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