[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
Official implementation for the paper: Generating Smooth Pose Sequences for Diverse Human Motion Prediction

Generating Smooth Pose Sequences for Diverse Human Motion Prediction This is official implementation for the paper Generating Smooth Pose Sequences fo

Wei Mao 28 Dec 10, 2022
traiNNer is an open source image and video restoration (super-resolution, denoising, deblurring and others) and image to image translation toolbox based on PyTorch.

traiNNer traiNNer is an open source image and video restoration (super-resolution, denoising, deblurring and others) and image to image translation to

202 Jan 04, 2023
PyTorch implementation of NeurIPS 2021 paper: "CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration"

PyTorch implementation of NeurIPS 2021 paper: "CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration"

76 Jan 03, 2023
An end-to-end implementation of intent prediction with Metaflow and other cool tools

You Don't Need a Bigger Boat An end-to-end (Metaflow-based) implementation of an intent prediction flow for kids who can't MLOps good and wanna learn

Jacopo Tagliabue 614 Dec 31, 2022
This repository contains the accompanying code for Deep Virtual Markers for Articulated 3D Shapes, ICCV'21

Deep Virtual Markers This repository contains the accompanying code for Deep Virtual Markers for Articulated 3D Shapes, ICCV'21 Getting Started Get sa

KimHyomin 45 Oct 07, 2022
Distributed Asynchronous Hyperparameter Optimization in Python

Hyperopt: Distributed Hyperparameter Optimization Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which

6.5k Jan 01, 2023
D-NeRF: Neural Radiance Fields for Dynamic Scenes

D-NeRF: Neural Radiance Fields for Dynamic Scenes [Project] [Paper] D-NeRF is a method for synthesizing novel views, at an arbitrary point in time, of

Albert Pumarola 291 Jan 02, 2023
NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

Göktuğ Karakaşlı 16 Dec 05, 2022
Learning hierarchical attention for weakly-supervised chest X-ray abnormality localization and diagnosis

Hierarchical Attention Mining (HAM) for weakly-supervised abnormality localization This is the official PyTorch implementation for the HAM method. Pap

Xi Ouyang 22 Jan 02, 2023
Exploiting a Zoo of Checkpoints for Unseen Tasks

Exploiting a Zoo of Checkpoints for Unseen Tasks This repo includes code to reproduce all results in the above Neurips paper, authored by Jiaji Huang,

Baidu Research 8 Sep 06, 2022
This project uses Template Matching technique for object detecting by detection of template image over base image.

Object Detection Project Using OpenCV This project uses Template Matching technique for object detecting by detection the template image over base ima

Pratham Bhatnagar 7 May 29, 2022
Autoencoder - Reducing the Dimensionality of Data with Neural Network

autoencoder Implementation of the Reducing the Dimensionality of Data with Neural Network – G. E. Hinton and R. R. Salakhutdinov paper. Notes Aim to m

Jordan Burgess 13 Nov 17, 2022
Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks

Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks

Jina AI 794 Dec 31, 2022
Bayesian Optimization using GPflow

Note: This package is for use with GPFlow 1. For Bayesian optimization using GPFlow 2 please see Trieste, a joint effort with Secondmind. GPflowOpt GP

GPflow 257 Dec 26, 2022
Code for ICE-BeeM paper - NeurIPS 2020

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA This repository contains code to run and reproduce the experiments

Ilyes Khemakhem 65 Dec 22, 2022
This is the repository of shape matching algorithm Iterative Rotations and Assignments (IRA)

Description This is the repository of shape matching algorithm Iterative Rotations and Assignments (IRA), described in the publication [1]. Directory

MAMMASMIAS Consortium 6 Nov 14, 2022
PyTorch Implementation of CycleGAN and SSGAN for Domain Transfer (Minimal)

MNIST-to-SVHN and SVHN-to-MNIST PyTorch Implementation of CycleGAN and Semi-Supervised GAN for Domain Transfer. Prerequites Python 3.5 PyTorch 0.1.12

Yunjey Choi 401 Dec 30, 2022
[CVPR 2020] 3D Photography using Context-aware Layered Depth Inpainting

[CVPR 2020] 3D Photography using Context-aware Layered Depth Inpainting [Paper] [Project Website] [Google Colab] We propose a method for converting a

Virginia Tech Vision and Learning Lab 6.2k Jan 01, 2023
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate. Website • Key Features • How To Use • Docs •

Pytorch Lightning 21.1k Dec 29, 2022
Implementation of C-RNN-GAN.

Implementation of C-RNN-GAN. Publication: Title: C-RNN-GAN: Continuous recurrent neural networks with adversarial training Information: http://mogren.

Olof Mogren 427 Dec 25, 2022