CS 6400: Advanced Topics in AI

Fall, 2024

Time: Thursday 4:00pm - 6:30pm

Location: CS Building Room-220

Huiyuan Yang

Email: hyang[at]mst[dot]edu
Office: Room-333 in CS Building
Office hours: Thursday, 2:00pm-4:00pm (CS Room-333)
Pre-lecture feedback meeting : Tuesday, 2:00pm-3:00pm
Grading Scheme (Roughly):
  • Presentation (30%)
  • Class Participation (15%)
  • Pre-lecture Questions (15%)
  • Course Project (40%)
Turning machine


Tentative Schedule:

Date

Topic

Papers

Pre-lecture questions

Presenters

Extra reading

08/22/2024

Introduction

 

08/29/2024

Models

ResNet

YOLO

§  Deep Residual Learning for Image Recognition

§  You Only Look Once: Unified, Real-Time Object Detection

 

09/05/2024

GCN

Transformer

§  Semi-Supervised Classification with Graph Convolutional Networks

§  Attention Is All You Need

 

09/12/2024

Generative Models

VAE

GAN

§  Auto-Encoding Variational Bayes

§  Generative Adversarial Nets

 

09/19/2024

Diffusion Models

§  Denoising Diffusion Probabilistic Models

§  Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

 

09/26/2024

Model Learning

Data Augmentation

§  mixup: Beyond Empirical Risk Minimization

§  AutoAugment: Learning Augmentation Strategies from Data

 

10/03/2024

Transfer Learning

§  Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

§  Taskonomy: Disentangling Task Transfer Learning

[Link] [Link]

10/10/2024

Fall Break

10/17/2024

Self-supervised Learning

§  A Simple Framework for Contrastive Learning of Visual Representations

§  Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning

 

10/24/2024

Data

Privacy & Fairness

Attack & Defense

§  A Survey on Bias and Fairness in Machine Learning

§  Explaining and Harnessing Adversarial Examples

 

10/31/2024

CV

CLIP

ViT

§  Learning Transferable Visual Models From Natural Language Supervision

§  An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

 

11/07/2024

LLM

BERT

GPT

§  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

§  Language Models are Few-Shot Learners

 

11/14/2024

Prompting

PEFT

§  Making Pre-trained Language Models Better Few-shot Learners

§  LoRA: Low-Rank Adaptation of Large Language Models

 

11/21/2024

Multimodal

Multimodal

§  Foundations & Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

§  Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action

 

11/28/2024

Thanksgiving

12/05/2024

Course Project Presentation & Demo