Search Results for author: Yu Gai

Found 8 papers, 3 papers with code

Blockchain Large Language Models

no code implementations25 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.

Anomaly Detection Intrusion Detection +2

Grounded Graph Decoding Improves Compositional Generalization in Question Answering

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.

Question Answering

Kokoyi: Executable LaTeX for End-to-end Deep Learning

no code implementations29 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.

Math Translation

Practical Convex Formulation of Robust One-hidden-layer Neural Network Training

no code implementations25 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.

Adversarial Robustness Binary Classification

Gradient-based learning for F-measure and other performance metrics

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.

General Classification

Loss Functions for Multiset Prediction

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.

Decision Making Reinforcement Learning (RL)

Cannot find the paper you are looking for? You can Submit a new open access paper.