no code implementations • 25 Apr 2023 • Yu Gai, Liyi Zhou, Kaihua Qin, Dawn Song, Arthur Gervais
This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions.
1 code implementation • Findings (EMNLP) 2021 • Yu Gai, Paras Jain, Wendi Zhang, Joseph E. Gonzalez, Dawn Song, Ion Stoica
Grounding enables the model to retain syntax information from the input in thereby significantly improving generalization over complex inputs.
no code implementations • 29 Sep 2021 • Minjie Wang, Haoming Lu, Yu Gai, Lesheng Jin, Zihao Ye, Zheng Zhang
Despite substantial efforts from the deep learning system community to relieve researchers and practitioners from the burden of implementing models with ever-growing complexity, a considerable lingual gap remains between developing models in the language of mathematics and implementing them in the languages of computer.
no code implementations • 25 May 2021 • Yatong Bai, Tanmay Gautam, Yu Gai, Somayeh Sojoudi
Recent work has shown that the training of a one-hidden-layer, scalar-output fully-connected ReLU neural network can be reformulated as a finite-dimensional convex program.
1 code implementation • NeurIPS 2020 • Jianfei Chen, Yu Gai, Zhewei Yao, Michael W. Mahoney, Joseph E. Gonzalez
We show that the FQT gradient is an unbiased estimator of the QAT gradient, and we discuss the impact of gradient quantization on its variance.
Ranked #9 on Semantic Textual Similarity on STS Benchmark
7 code implementations • 3 Sep 2019 • Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, Zheng Zhang
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.
Ranked #35 on Node Classification on Cora
no code implementations • ICLR 2019 • Yu Gai, Zheng Zhang, Kyunghyun Cho
Many important classification performance metrics, e. g. $F$-measure, are non-differentiable and non-decomposable, and are thus unfriendly to gradient descent algorithm.
no code implementations • ICLR 2018 • Sean Welleck, Zixin Yao, Yu Gai, Jialin Mao, Zheng Zhang, Kyunghyun Cho
In this paper, we propose a novel multiset loss function by viewing this problem from the perspective of sequential decision making.