50 citations · 80 across the 5 of their papers we have counts for
10 papers
Transformer with Memory Replay
Rui Liu, Barzan Mozafari
Transformers achieve state-of-the-art performance for natural language processing tasks by pre-training on large-scale text corpora. They are extremely compute-intensive and have v…
Adam with Bandit Sampling for Deep Learning
Rui Liu, Tianyi Wu, Barzan Mozafari
Adam is a widely used optimization method for training deep learning models. It computes individual adaptive learning rates for different parameters. In this paper, we propose a ge…
Joins on Samples: A Theoretical Guide for Practitioners
Dawei Huang, Dong Young Yoon, Seth Pettie +1
Despite decades of research on approximate query processing (AQP), our understanding of sample-based joins has remained limited and, to some extent, even superficial. The common be…
BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees
Yongjoo Park, Jingyi Qing, Xiaoyang Shen +1
The rising volume of datasets has made training machine learning (ML) models a major computational cost in the enterprise. Given the iterative nature of model and parameter tuning,…
A Bandit Approach to Maximum Inner Product Search
Rui Liu, Tianyi Wu, Barzan Mozafari
There has been substantial research on sub-linear time approximate algorithms for Maximum Inner Product Search (MIPS). To achieve fast query time, state-of-the-art techniques requi…
QuickSel: Quick Selectivity Learning with Mixture Models
Yongjoo Park, Shucheng Zhong, Barzan Mozafari
Estimating the selectivity of a query is a key step in almost any cost-based query optimizer. Most of today's databases rely on histograms or samples that are periodically refreshe…