Fei Wang (FeiWang96)

FeiWang96

Geek Repo

Company:University of Southern California

Location:Los Angeles

Home Page:feiwang96.github.io

Twitter:@fwang_nlp

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Fei Wang's repositories

GTR

[SIGIR 2021] Retrieving Complex Tables with Multi-Granular Graph Representation Learning.

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CorefQA-pytorch

A PyTorch implementation of the CorefQA Model.

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FeiWang96.github.io

A beautiful, simple, clean, and responsive Jekyll theme for academics

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academic-kickstart

📝 Easily create a beautiful website using Academic, Hugo, and Netlify

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awesome-automl-papers

A curated list of automated machine learning papers, articles, tutorials, slides and projects

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CompGCN

ICLR 2020: Composition-Based Multi-Relational Graph Convolutional Networks

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CompGCN-DGL

An implement of CompGCN in Pytorch and DGL.

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Contrastive_Learning_Papers

A list of contrastive Learning papers

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CorefQA

Code base for the paper `CorefQA: Coreference Resolution as Query-based Span Prediction`

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CorefQA-1

This repo contains the code for ACL2020 paper "Coreference Resolution as Query-based Span Prediction"

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DeepLearningExamples

Deep Learning Examples

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DPR

Dense Passage Retriever - is a set of tools and models for open domain Q&A task.

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effective_transformer

Running BERT without Padding

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EntityQuestions

EMNLP'2021: Simple Entity-centric Questions Challenge Dense Retrievers

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IJCAI2019_HGAT

Semi-supervised User Profiling with Heterogeneous Graph Attention Networks, IJCAI 2019.

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MIND

Microsoft News Dataset

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MLMET_Reproduce

USC CSCI 662 Project

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nlp-architect

A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks

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PromptPapers

Must-read papers on prompt-based tuning for pre-trained language models.

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pytorch-transformers

👾 A library of state-of-the-art pretrained models for Natural Language Processing (NLP)

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tapas

End-to-end neural table-text understanding models.

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ToTTo

ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. We hope it can serve as a useful research benchmark for high-precision conditional text generation.

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zhihuhelp

基于node&typescript重写知乎助手

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