[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
Mosaic of Object-centric Images as Scene-centric Images (MosaicOS) for long-tailed object detection and instance segmentation.

MosaicOS Mosaic of Object-centric Images as Scene-centric Images (MosaicOS) for long-tailed object detection and instance segmentation. Introduction M

Cheng Zhang 27 Oct 12, 2022
A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).

Attention Walk ⠀⠀ A PyTorch Implementation of Watch Your Step: Learning Node Embeddings via Graph Attention (NIPS 2018). Abstract Graph embedding meth

Benedek Rozemberczki 303 Dec 09, 2022
Code for reproducing our paper: LMSOC: An Approach for Socially Sensitive Pretraining

LMSOC: An Approach for Socially Sensitive Pretraining Code for reproducing the paper LMSOC: An Approach for Socially Sensitive Pretraining to appear a

Twitter Research 11 Dec 20, 2022
Extreme Dynamic Classifier Chains - XGBoost for Multi-label Classification

Extreme Dynamic Classifier Chains Classifier chains is a key technique in multi-label classification, sinceit allows to consider label dependencies ef

6 Oct 08, 2022
Code Repository for The Kaggle Book, Published by Packt Publishing

The Kaggle Book Data analysis and machine learning for competitive data science Code Repository for The Kaggle Book, Published by Packt Publishing "Lu

Packt 1.6k Jan 07, 2023
Official PyTorch Implementation of Convolutional Hough Matching Networks, CVPR 2021 (oral)

Convolutional Hough Matching Networks This is the implementation of the paper "Convolutional Hough Matching Network" by J. Min and M. Cho. Implemented

Juhong Min 70 Nov 22, 2022
N-gram models- Unsmoothed, Laplace, Deleted Interpolation

N-gram models- Unsmoothed, Laplace, Deleted Interpolation

Ravika Nagpal 1 Jan 04, 2022
Codes for 'Dual Parameterization of Sparse Variational Gaussian Processes'

Dual Parameterization of Sparse Variational Gaussian Processes Documentation | Notebooks | API reference Introduction This repository is the official

AaltoML 7 Dec 23, 2022
😇A pyTorch implementation of the DeepMoji model: state-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc

------ Update September 2018 ------ It's been a year since TorchMoji and DeepMoji were released. We're trying to understand how it's being used such t

Hugging Face 865 Dec 24, 2022
Neural style in TensorFlow! 🎨

neural-style An implementation of neural style in TensorFlow. This implementation is a lot simpler than a lot of the other ones out there, thanks to T

Anish Athalye 5.5k Dec 29, 2022
abess: Fast Best-Subset Selection in Python and R

abess: Fast Best-Subset Selection in Python and R Overview abess (Adaptive BEst Subset Selection) library aims to solve general best subset selection,

297 Dec 21, 2022
Faster Convex Lipschitz Regression

Faster Convex Lipschitz Regression This reepository provides a python implementation of our Faster Convex Lipschitz Regression algorithm with GPU and

Ali Siahkamari 0 Nov 19, 2021
Automatic deep learning for image classification.

AutoDL AutoDL automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few line

wenqi 2 Oct 12, 2022
EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation

EFENet EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation Code is a bit messy now. I woud clean up soon. For training the EF

Yaping Zhao 19 Nov 05, 2022
[CVPR 2022 Oral] Versatile Multi-Modal Pre-Training for Human-Centric Perception

Versatile Multi-Modal Pre-Training for Human-Centric Perception Fangzhou Hong1  Liang Pan1  Zhongang Cai1,2,3  Ziwei Liu1* 1S-Lab, Nanyang Technologic

Fangzhou Hong 96 Jan 03, 2023
Learning Intents behind Interactions with Knowledge Graph for Recommendation, WWW2021

Learning Intents behind Interactions with Knowledge Graph for Recommendation This is our PyTorch implementation for the paper: Xiang Wang, Tinglin Hua

158 Dec 15, 2022
Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning Source Code

Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning Trevor Ablett*, Bryan Chan*,

STARS Laboratory 8 Sep 14, 2022
Unsupervised Representation Learning via Neural Activation Coding

Neural Activation Coding This repository contains the code for the paper "Unsupervised Representation Learning via Neural Activation Coding" published

yookoon park 5 May 26, 2022
WORD: Revisiting Organs Segmentation in the Whole Abdominal Region

WORD: Revisiting Organs Segmentation in the Whole Abdominal Region (Paper and DataSet). [New] Note that all the emails about the download permission o

Healthcare Intelligence Laboratory 71 Dec 22, 2022
Nvdiffrast - Modular Primitives for High-Performance Differentiable Rendering

Nvdiffrast – Modular Primitives for High-Performance Differentiable Rendering Modular Primitives for High-Performance Differentiable Rendering Samuli

NVIDIA Research Projects 675 Jan 06, 2023