[AI6122] Text Data Management & Processing

Overview

[AI6122] Text Data Management & Processing

====== I M P O R T A N T ======

The content in this repository should exclusively be utilized in sharing solutions for projects, communicating ideas for related problems, and references to similar assignments. If you are a student facing an assignment with the same or similar topics, you can use this repository as a reference, while the final report should include the citations of the repository. If you submit an assignment without proper acknowledgment after referring to this repository, you may be considered PLAGIARISM by your instructor, and the author will not pay ANY responsibility for this. Please refer to your teacher's and your school's instructions for the determination of academic integrity.

Moreover, if you are taking the AI6122 course, do not be stupid. You can utilize the materials here as a reference to construct your own assignment and reflect the citation to this repository in the final report. If you copy the code without citing it, you have violated NTU's academic integrity and are involved in plagiarism.

Please refer to the following links for NTU's determination of academic integrity and plagiarism:

https://ts.ntu.edu.sg/sites/intranet/dept/tlpd/ai/Pages/NTU-Academic-Integrity-Policy.aspx

https://ts.ntu.edu.sg/sites/intranet/dept/tlpd/ai/Pages/default.aspx

https://ts.ntu.edu.sg/sites/policyportal/new/Documents/All%20including%20NIE%20staff%20and%20students/Student%20Academic%20Integrity%20Policy.pdf

If you think the professor is easy to fool, think again.
You know who you are.

====== D I S C L A I M E R ======

This repository should only be used for reasonable academic discussions. I, the owner of this repository, never and will never ALLOWING another student to copy this assignment as their own. In such circumstances, I do not violate NTU's statement on academic integrity as of the time this repository is open (18/01/2022). I am not responsible for any future plagiarism using the content of this repository.



====== I N T R O D U C T I O N ======

[AI6122] Text Data Management & Processing is an elective course of Master of Science in Artificial Intelligence Graduate Programme (MSAI), School of Computer Science and Engineering (SCSE), Nanyang Technological University (NTU), Singapore. The repository corresponds to the AI6122 of Semester 1, AY2021-2022, starting from 08/2021. The instructor of this course is Prof. Sun Aixin.

The projects of this course consist of one individual Literature Review, and one group Project. The topic of them are shown below, and we do not have the specific grade of them given by the prof. Since multiple people complete the group work, I do not have the right to disclose the report and others' codes individually so that the relevant parts will be hidden, and the group project only presents part of the code and report finished by myself.

Type Topic Grade
Literature Review Chinese Spelling Check N.A. / 30.0
Group Project Data Analysis and Processing N.A. / 40.0
Quiz N.A. N.A. / 30.0

====== A C K N O W L E D G E M E N T ======

All of above projects are designed by Prof. Sun Aixin.

Owner
HT. Li
HT. Li
PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"

Contrast to Divide: self-supervised pre-training for learning with noisy labels This is an official implementation of "Contrast to Divide: self-superv

55 Nov 23, 2022
PyGRANSO: A PyTorch-enabled port of GRANSO with auto-differentiation

PyGRANSO PyGRANSO: A PyTorch-enabled port of GRANSO with auto-differentiation Please check https://ncvx.org/PyGRANSO for detailed instructions (introd

SUN Group @ UMN 26 Nov 16, 2022
Tutorial page of the Climate Hack, the greatest hackathon ever

Tutorial page of the Climate Hack, the greatest hackathon ever

UCL Artificial Intelligence Society 12 Jul 02, 2022
MaskTrackRCNN for video instance segmentation based on mmdetection

MaskTrackRCNN for video instance segmentation Introduction This repo serves as the official code release of the MaskTrackRCNN model for video instance

411 Jan 05, 2023
Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps[AAAI2021]

Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps Here is the code for ssbassline model. We also provide OCR results/features/mode

ZephyrZhuQi 51 Nov 18, 2022
High-quality implementations of standard and SOTA methods on a variety of tasks.

Uncertainty Baselines The goal of Uncertainty Baselines is to provide a template for researchers to build on. The baselines can be a starting point fo

Google 1.1k Dec 30, 2022
A semismooth Newton method for elliptic PDE-constrained optimization

sNewton4PDEOpt The Python module implements a semismooth Newton method for solving finite-element discretizations of the strongly convex, linear ellip

2 Dec 08, 2022
LaneDetectionAndLaneKeeping - Lane Detection And Lane Keeping

LaneDetectionAndLaneKeeping This project is part of my bachelor's thesis. The go

5 Jun 27, 2022
A library that can print Python objects in human readable format

objprint A library that can print Python objects in human readable format Install pip install objprint Usage op Use op() (or objprint()) to print obj

319 Dec 25, 2022
Code for ICLR2018 paper: Improving GAN Training via Binarized Representation Entropy (BRE) Regularization - Y. Cao · W Ding · Y.C. Lui · R. Huang

code for "Improving GAN Training via Binarized Representation Entropy (BRE) Regularization" (ICLR2018 paper) paper: https://arxiv.org/abs/1805.03644 G

21 Oct 12, 2020
PuppetGAN - Cross-Domain Feature Disentanglement and Manipulation just got way better! 🚀

Better Cross-Domain Feature Disentanglement and Manipulation with Improved PuppetGAN Quite cool... Right? Introduction This repo contains a TensorFlow

Giorgos Karantonis 5 Aug 25, 2022
This is the code for the paper "Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei: Gait Recognition in the Wild with Dense 3D Representations and A Benchmark. (CVPR 2022)"

Gait3D-Benchmark This is the code for the paper "Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei: Gait Recognition in the Wild

82 Jan 04, 2023
Bayesian Image Reconstruction using Deep Generative Models

Bayesian Image Reconstruction using Deep Generative Models R. Marinescu, D. Moyer, P. Golland For technical inquiries, please create a Github issue. F

Razvan Valentin Marinescu 51 Nov 23, 2022
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search

DropNAS: Grouped Operation Dropout for Differentiable Architecture Search DropNAS, a grouped operation dropout method for one-level DARTS, with better

weijunhong 4 Aug 15, 2022
Adapter-BERT: Parameter-Efficient Transfer Learning for NLP.

Adapter-BERT: Parameter-Efficient Transfer Learning for NLP.

Google Research 340 Jan 03, 2023
The toolkit to generate auto labeled datasets

Ozeu Ozeu is the toolkit to autolabal dataset for instance segmentation. You can generate datasets labaled with segmentation mask and bounding box fro

Xiong Jie 28 Mar 28, 2022
Pyramid Grafting Network for One-Stage High Resolution Saliency Detection. CVPR 2022

PGNet Pyramid Grafting Network for One-Stage High Resolution Saliency Detection. CVPR 2022, CVPR 2022 (arXiv 2204.05041) Abstract Recent salient objec

CVTEAM 109 Dec 05, 2022
AdamW optimizer for bfloat16 models in pytorch.

Image source AdamW optimizer for bfloat16 models in pytorch. Bfloat16 is currently an optimal tradeoff between range and relative error for deep netwo

Alex Rogozhnikov 8 Nov 20, 2022
The source code of CVPR17 'Generative Face Completion'.

GenerativeFaceCompletion Matcaffe implementation of our CVPR17 paper on face completion. In each panel from left to right: original face, masked input

Yijun Li 313 Oct 18, 2022
Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

Demetri Pananos 9 Oct 04, 2022