252 citations · 1.3k across the 10 of their papers we have counts for
10 papers
Balsa: Learning a Query Optimizer Without Expert Demonstrations
Zongheng Yang, Wei-Lin Chiang, Sifei Luan +3
Query optimizers are a performance-critical component in every database system. Due to their complexity, optimizers take experts months to write and years to refine. In this work,…
Variable Skipping for Autoregressive Range Density Estimation
Eric Liang, Zongheng Yang, Ion Stoica +3
Deep autoregressive models compute point likelihood estimates of individual data points. However, many applications (i.e., database cardinality estimation) require estimating range…
NeuroCard: One Cardinality Estimator for All Tables
Zongheng Yang, Amog Kamsetty, Sifei Luan +4
Query optimizers rely on accurate cardinality estimates to produce good execution plans. Despite decades of research, existing cardinality estimators are inaccurate for complex que…
Qd-tree: Learning Data Layouts for Big Data Analytics
Zongheng Yang, Badrish Chandramouli, Chi Wang +6
Corporations today collect data at an unprecedented and accelerating scale, making the need to run queries on large datasets increasingly important. Technologies such as columnar b…
Deep Unsupervised Cardinality Estimation
Zongheng Yang, Eric Liang, Amog Kamsetty +7
Cardinality estimation has long been grounded in statistical tools for density estimation. To capture the rich multivariate distributions of relational tables, we propose the use o…
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…
Learning to Optimize Join Queries With Deep Reinforcement Learning
Sanjay Krishnan, Zongheng Yang, Ken Goldberg +2
Exhaustive enumeration of all possible join orders is often avoided, and most optimizers leverage heuristics to prune the search space. The design and implementation of heuristics…
Ray: A Distributed Framework for Emerging AI Applications
Philipp Moritz, Robert Nishihara, Stephanie Wang +8
The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requir…
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
Jonathan Shen, Ruoming Pang, Ron J. Weiss +10
This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. The system is composed of a recurrent sequence-to-sequence feature predictio…
Tacotron: Towards End-to-End Speech Synthesis
Yuxuan Wang, RJ Skerry-Ryan, Daisy Stanton +11
A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module. Building these component…