Hi-ZenanXu

AGGCN_TACRED

Attention Guided Graph Convolutional Networks for Relation Extraction (authors' PyTorch implementation for the ACL19 paper)

capstone

Capstone disassembly/disassembler framework: Core (Arm, Arm64, BPF, EVM, M68K, M680X, MOS65xx, Mips, PPC, RISCV, Sparc, SystemZ, TMS320C64x, Web Assembly, X86, X86_64, XCore) + bindings.

dataset

EBM--Generative-Energy-Based-Modeling

Energy Based Models are a quite novel technique for density estimation. In this university project I explore this new research topic and implement EBMs as generative models, comparing the results obtained with Maximum Likelihood estimation and Sliced Score Matching on MNIST and a toy 2D dataset,

Hi-ZenanXu.github.io

OUCML

Syntax-Enhanced_Pre-trained_Model

Source Data of ACL2021 paper "Syntax-Enhanced Pre-trained Model"

text_gcn

Graph Convolutional Networks for Text Classification. AAAI 2019

tutorial

Tutorial on ‘Graph-Based Meaning Representations: Design and Processing’ (ACL 2019)

wlwz

武林外传外辅源码

Hi-ZenanXu/Syntax-Enhanced_Pre-trained_Model

Source Data of ACL2021 paper "Syntax-Enhanced Pre-trained Model"

Hi-ZenanXu/AGGCN_TACRED

Attention Guided Graph Convolutional Networks for Relation Extraction (authors' PyTorch implementation for the ACL19 paper)

Hi-ZenanXu/capstone

Capstone disassembly/disassembler framework: Core (Arm, Arm64, BPF, EVM, M68K, M680X, MOS65xx, Mips, PPC, RISCV, Sparc, SystemZ, TMS320C64x, Web Assembly, X86, X86_64, XCore) + bindings.

Hi-ZenanXu/dataset

Hi-ZenanXu/EBM--Generative-Energy-Based-Modeling

Energy Based Models are a quite novel technique for density estimation. In this university project I explore this new research topic and implement EBMs as generative models, comparing the results obtained with Maximum Likelihood estimation and Sliced Score Matching on MNIST and a toy 2D dataset,

Hi-ZenanXu/Hi-ZenanXu.github.io

Hi-ZenanXu/OUCML

Hi-ZenanXu/text_gcn

Graph Convolutional Networks for Text Classification. AAAI 2019

Hi-ZenanXu/tutorial

Tutorial on ‘Graph-Based Meaning Representations: Design and Processing’ (ACL 2019)

Hi-ZenanXu/wlwz

武林外传外辅源码