Search Results for author: Mingzhe Wang

Found 9 papers, 4 papers with code

Network-Wide Task Offloading With LEO Satellites: A Computation and Transmission Fusion Approach

no code implementations16 Nov 2022 Jiaqi Cao, Shengli Zhang, Qingxia Chen, Houtian Wang, Mingzhe Wang, Naijin Liu

To address the network-wide offloading problem, we propose a metagraph-based computation and transmission fusion offloading scheme for multi-tier networks.

A Unified Framework of Surrogate Loss by Refactoring and Interpolation

1 code implementation ECCV 2020 Lanlan Liu, Mingzhe Wang, Jia Deng

We introduce UniLoss, a unified framework to generate surrogate losses for training deep networks with gradient descent, reducing the amount of manual design of task-specific surrogate losses.

Speaker Naming in Movies

no code implementations NAACL 2018 Mahmoud Azab, Mingzhe Wang, Max Smith, Noriyuki Kojima, Jia Deng, Rada Mihalcea

We propose a new model for speaker naming in movies that leverages visual, textual, and acoustic modalities in an unified optimization framework.

EnFuzz: From Ensemble Learning to Ensemble Fuzzing

no code implementations30 Jun 2018 Yuanliang Chen, Yu Jiang, Jie Liang, Mingzhe Wang, Xun Jiao

For evaluation, we implement EnFuzz , a prototype basing on four strong open-source fuzzers (AFL, AFLFast, AFLGo, FairFuzz), and test them on Google's fuzzing test suite, which consists of widely used real-world applications.

Software Engineering

Premise Selection for Theorem Proving by Deep Graph Embedding

1 code implementation NeurIPS 2017 Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng

We propose a deep learning-based approach to the problem of premise selection: selecting mathematical statements relevant for proving a given conjecture.

Automated Theorem Proving General Classification +1

LINE: Large-scale Information Network Embedding

8 code implementations12 Mar 2015 Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction.

Graph Embedding Link Prediction +2

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