[AI6101] Introduction to AI & AI Ethics is a core course of MSAI, SCSE, NTU, Singapore

Overview

[AI6101] Introduction to AI & AI Ethics

====== 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 AI6101 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.

====== 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 (16/01/2022). 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 ======

[AI6101] Introduction to AI & AI Ethics is a core 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 AI6101 of Semester 1, AY2021-2022, starting from 08/2021. The instructors of this course are Prof. Bo An, Prof. Yu Han, and Dr. Melvin Chen.

The projects of this course consist of one individual Assignments, one individual Eassy, and one group Project. The topic of the assignment are shown below, and the specific score is not provided to us. 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.

Type Topic
Assignment Reinforcement Learning
Eassy Normative Theory
Group Project Responsible AI

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

All of above projects are designed by Prof. Bo An, Prof. Yu Han, and Dr. Melvin Chen.

Owner
AccSrd
AccSrd
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation (CVPR 2022)

CCAM (Unsupervised) Code repository for our paper "CCAM: Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localizati

Computer Vision Insitute, SZU 113 Dec 27, 2022
Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images (ICCV 2021)

Table of Content Introduction Getting Started Datasets Installation Experiments Training & Testing Pretrained models Texture fine-tuning Demo Toward R

VinAI Research 42 Dec 05, 2022
✅ How Robust are Fact Checking Systems on Colloquial Claims?. In NAACL-HLT, 2021.

How Robust are Fact Checking Systems on Colloquial Claims? Official PyTorch implementation of our NAACL paper: Byeongchang Kim*, Hyunwoo Kim*, Seokhee

Byeongchang Kim 19 Mar 15, 2022
Cmsc11 arcade - Final Project for CMSC11

cmsc11_arcade Final Project for CMSC11 Developers: Limson, Mark Vincent Peñafiel

Gregory 1 Jan 18, 2022
Lightweight mmm - Lightweight (Bayesian) Media Mix Model

Lightweight (Bayesian) Media Mix Model This is not an official Google product. L

Google 342 Jan 03, 2023
Tensorflow 2.x implementation of Vision-Transformer model

Vision Transformer Unofficial Tensorflow 2.x implementation of the Transformer based Image Classification model proposed by the paper AN IMAGE IS WORT

Soumik Rakshit 16 Jul 20, 2022
Fast Neural Representations for Direct Volume Rendering

Fast Neural Representations for Direct Volume Rendering Sebastian Weiss, Philipp Hermüller, Rüdiger Westermann This repository contains the code and s

Sebastian Weiss 20 Dec 03, 2022
Bare bones use-case for deploying a containerized web app (built in streamlit) on AWS.

Containerized Streamlit web app This repository is featured in a 3-part series on Deploying web apps with Streamlit, Docker, and AWS. Checkout the blo

Collin Prather 62 Jan 02, 2023
Object detection, 3D detection, and pose estimation using center point detection:

Objects as Points Object detection, 3D detection, and pose estimation using center point detection: Objects as Points, Xingyi Zhou, Dequan Wang, Phili

Xingyi Zhou 6.7k Jan 03, 2023
A symbolic-model-guided fuzzer for TLS

tlspuffin TLS Protocol Under FuzzINg A symbolic-model-guided fuzzer for TLS Master Thesis | Thesis Presentation | Documentation Disclaimer: The term "

69 Dec 20, 2022
Research - dataset and code for 2016 paper Learning a Driving Simulator

the people's comma the paper Learning a Driving Simulator the comma.ai driving dataset 7 and a quarter hours of largely highway driving. Enough to tra

comma.ai 4.1k Jan 02, 2023
A Pose Estimator for Dense Reconstruction with the Structured Light Illumination Sensor

Phase-SLAM A Pose Estimator for Dense Reconstruction with the Structured Light Illumination Sensor This open source is written by MATLAB Run Mode Open

Xi Zheng 14 Dec 19, 2022
Self-Supervised depth kalilia

Self-Supervised depth kalilia

24 Oct 15, 2022
Code release for "BoxeR: Box-Attention for 2D and 3D Transformers"

BoxeR By Duy-Kien Nguyen, Jihong Ju, Olaf Booij, Martin R. Oswald, Cees Snoek. This repository is an official implementation of the paper BoxeR: Box-A

Nguyen Duy Kien 111 Dec 07, 2022
Keras implementations of Generative Adversarial Networks.

This repository has gone stale as I unfortunately do not have the time to maintain it anymore. If you would like to continue the development of it as

Erik Linder-Norén 8.9k Jan 04, 2023
[NeurIPS-2020] Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID.

Self-paced Contrastive Learning (SpCL) The official repository for Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID

Yixiao Ge 286 Dec 21, 2022
[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data (NeurIPS 2021) This repository will provide the official PyTorch implementa

Liming Jiang 238 Nov 25, 2022
Spectrum is an AI that uses machine learning to generate Rap song lyrics

Spectrum Spectrum is an AI that uses deep learning to generate rap song lyrics. View Demo Report Bug Request Feature Open In Colab About The Project S

39 Dec 16, 2022
PyTorch Implement for Path Attention Graph Network

SPAGAN in PyTorch This is a PyTorch implementation of the paper "SPAGAN: Shortest Path Graph Attention Network" Prerequisites We prefer to create a ne

Yang Yiding 38 Dec 28, 2022
Objax Apache-2Objax (🥉19 · ⭐ 580) - Objax is a machine learning framework that provides an Object.. Apache-2 jax

Objax Tutorials | Install | Documentation | Philosophy This is not an officially supported Google product. Objax is an open source machine learning fr

Google 729 Jan 02, 2023