Paper Review
Paper Review
Paper Review Seminar (2024)
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers (Kyuchul Lee)
Youtube: https://youtu.be/o5X0oMSvbhU
Multimodal Learning with Transformers: A Survey (Dohoon Kim)
Youtube: https://youtu.be/Rqinkl18kXk
Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting (Changyu Jeon)
Youtube: https://youtu.be/HBmm8HSVONs
Segment Anything (Kyumin Khang)
Youtube: https://youtu.be/42gj9ej3Eqc
Dynamic Anchor Boxes are Better Queries for DETR (Byeonghwa Lee)
Youtube: https://youtu.be/mzKPmRaIWSQ
Distilling the Knowledge in a Neural Network (Junhyeok Choi)
Youtube: https://youtu.be/RoFvmGe9Gv8
Time2Vec: Learning a Vector Representation of Time (Jiyeon Lee)
Youtube: https://youtu.be/HAYqAPVRcTI
Enhancing Model Performance And Interpretability Through Influence-Based Data Selection (Chaeyun Yeo)
Youtube: https://youtu.be/bTlCTbqYzuw
TSMixer : Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting (Changyu Jeon)
Youtube: https://youtu.be/JtnBfQnIFlM
Time Series Forecasting Using Various Deep Learning Models (Tae Kyoung Lee)
Youtube: https://youtu.be/3FfHMj6PdW0
CoCa: Contrastive Captioners are Image-Text Foundation Models (Kyumin Khang)
Youtube: https://youtu.be/3IwXO7-TR-A
ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis (Kyuchul Lee)
Youtube: https://youtu.be/BRzrZtiDfHM
Patch SVDD: Patch-Level SVDD for Anomaly Detection and Segmentation (Yu Ha Lee)
Youtube: https://youtu.be/JYLPPk2aFg4
VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation (Byeonghwa Lee)
Youtube: https://youtu.be/qwwTcqtpTRg
Physics-Informed Neural Networks: A deep Learning Framework for solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations (Junhyeok Choi)
Youtube: https://youtu.be/pSbuI-wB5Sg
AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks (Inyeol Choi)
Youtube: https://youtu.be/T4vfJk9gT90
Differentiable Convex Optimization Layers (Jiyeon Lee)
Youtube: https://youtu.be/nx6_RinZsGY
Score-Based Generative Modeling through Stochastic Differential Equations (Dohoon Kim)
Youtube: https://youtu.be/w2foiszx5lo
Respect the Model Fine-Grained and Robust Explanation with Sharing Ratio Decomposition (Chaeyun Yeo)
Youtube: https://youtu.be/29sPCq5AeIY
Attention is All You Need (Seongjoon Yoon)
Youtube: https://youtu.be/TX6OQmEaZAM
Data-Driven State of Charge Estimation for Li Battery Packs based on GPR (Changyu Jeon)
Youtube: https://youtu.be/M-5cdiMj-kw
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games (Donghui Lim)
Youtube: https://youtu.be/m85RtoIHLVg
Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Kyuchul Lee)
Youtube: https://youtu.be/fURRobLWkqc
Llm in a flash: Efficient large language model inference with limited memory (Kyumin Khang)
Youtube: https://youtu.be/9AZ067J1O_o
Paper Review Seminar (2023)
The Curse of Recursion: Training on Generated Data Makes Models Forget (Suyeong Lim)
Youtube: https://youtu.be/LwijXgZ9Mxg
Distribution Aligning Refinery of Psuedo-label for Imbalanced Semi-supervised Learning (Sangjae Lee)
Youtube: https://youtu.be/_fTH5iFxZ6c
Zero-shot Text-to-image Generation (Myunghyun Lee)
Youtube: https://youtu.be/rRwwIn07UBM
TapNet: Multivariate Time Series Classification with Attentional Prototypical Network (Hyemin Han)
Youtube: https://youtu.be/RT00AllOhNE
Language Models are Few Shot Learners and GPT Practical Tips (Kyu Min Khang)
Youtube: https://youtu.be/UMQ227ANobc
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy (Kyuchul Lee)
Youtube: https://youtu.be/1ggV-0Y0rkE
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (Cheyun Yeo)
Youtube: https://youtu.be/OVKSrGuc9v8
An Image is worth 16x16 words: transformers for image recognition at scale (Hyung Won Kim)
Youtube: https://youtu.be/HreXqenSM9g
A Comprehensive Survey of Recent Trends in Data Augmentation (Kyu Min Khang)
Youtube: https://youtu.be/Im1bHL0oyDY
Memorizing Normality to Detect Anomaly (Kyuchul Lee)
Youtube: https://youtu.be/v8ES9azY7lA
Usad: Unsupervised anomaly detection on multivariate time series (Chaeyun Yeo)
Youtube: https://youtu.be/rkPwOWYeqhk
A Simple Framework for Contrastive Learning of Visual Representations (Hyung Won Kim)
Youtube: https://youtu.be/BHmJEvkN8ZU
Auto-encoding variational bayes (Kyuchul Lee)
Youtube: https://youtu.be/inqCpKKipr8
Time Series Forecasting Using Various Deep Learning Models (Chaeyun Yeo)
Youtube: https://youtu.be/qYwxTZLQFso
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision (Hyung Won Kim)
Youtube: https://youtu.be/QFCkwCg8Z98
Paper Review Seminar (2022)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Jae Young Kim)
Youtube: https://youtu.be/2AvoJPhrOQY
Efficient NetV2: Smaller Models and Faster Training (Chaeyun Yeo)
Youtube: https://youtu.be/T10Fck86U0I
Efficient Net: Rethinking Model Scaling for Convolutional Neural Networks (Chaeyun Yeo)
Youtube: https://youtu.be/3J0AHnLK82c
DETR: End-to-End Object Detection with Transformer (Doo Jin Lee)
Youtube: https://youtu.be/U7FlIvorkR0
CNC Machine AI Practice (Shin Hoon Kang)
Youtube: https://youtu.be/JtXza8ooX3s
A Machine Learning Algorithm to Estimate the Probability of a True Scaphoid Fracture after Wrist Trauma (Hyung Won Kim)
Youtube: https://youtu.be/hAUYdjbTajo
Deep High Resolution Representation Learning for Human Pose Estimation (Jae Young Kim)
Youtube: https://youtu.be/jfTYYsOa3nE
An Image is Worth 16 x 16 Words: Transformers for Image Recognition at Scale (Jae Young Kim)
Youtube: https://youtu.be/tlz8NmbOQ80
Deep Learning Model Study for Bitcoin Price Prediction (Presenter: Hyung Won Kim)
Youtube: https://youtu.be/uSUwES-d4Fo
ImageNet Classifciation with Deep Convolutional Neural Network (Presenter: Doo Jin Lee)
Youtube: https://youtu.be/olqWSIDCsDY
Data Analysis for CNC Machine Operation (2) (Presenter: Shin Hoon Kang)
Youtube: https://youtu.be/3auc8GYUP6Y
You Only Look Once: Unified, Real-time Object Detection (Presenter: Doo Jin Lee)
Youtube: https://youtu.be/OZtEL9dydLM
Attention is All You Need (Presenter: Jae Young Kim)
Youtube: https://youtu.be/fm70Me6IxrM
Multi-classification Deep CNN Model for Diagnosing COVID (Presenter: Hyung Won Kim)
Youtube: https://youtu.be/KE15QUkKVTg
Data Analysis for CNC Machine Operation (Presenter: Shin Hoon Kang)
Youtube: https://youtu.be/WKzQAo7wB18
Book Review
Chapter 1. An Introduction to Large Language Models (Chaeyun Yeo)
Youtube: https://youtu.be/88a2K1qRYAs
Chapter 2. Tokens and Embedding (Byunghwa Lee)
Youtube: https://youtu.be/j4HOkYHER3Q
Chapter 3. Introduction to Large Language Models (Yuha Lee)
Youtube: https://youtu.be/qiZwTh0DyI8
Chapter 4. Text Classification (Kyumin Khang)
Youtube: https://youtu.be/rGn1BqWZ-u4
Chapter 5. Text Clustering and Topic Modeling (Junhyeok Choi)
Youtube: https://youtu.be/4Epc68RHc8E
Chapter 6. Prompt Engineering (Changyu Jeon)
Youtube: https://youtu.be/yoKOi7VQbyc
Chapter 7. Advanced Text Generation Techniques and Tools (Kyuchul Lee)
Youtube: https://youtu.be/QUos18mT8sQ
Chapter 8. Semantic Search and RAG (Dohoon Kim)
Youtube: https://youtu.be/T-EDpzRDQL4
Chapter 9. multimodal large language model (Woongjae Na)
Youtube: https://youtu.be/2Icz-kVsXwg
Chapter 10. Text Embedding (Hawon Na)
Youtube: https://youtu.be/VHCgp9Tsvbo
Chapter 11. Fine Tuning for Representation Models for Classification (Jaejun Choi)
Youtube: https://youtu.be/dOifuWXkKQc
Chapter 12. Fine Tuning Generation Models (Hyunjong Chang)
Youtube: https://youtu.be/vW85KDuEj30
Chapter 1. Introduction (Kyu Min Khang, Suyeong Lim)
Youtube: https://youtu.be/HxDTMn6HJPw
Chapter 2. Deep Learning (Kyu Min Khang, Eunkyeong Lee)
Youtube: https://youtu.be/eGJ6e5iG5zc
Chapter 3. Variational Autoencoder (Kyuchul Lee, Myunghyun Lee)
Youtube: https://youtu.be/TJbY0-d9p5g
Chapter 4. Generative Adversarial Network (Cheyun Yeo, Hyemin Han)
Youtube: https://youtu.be/WTyhAgc4LEs
Chapter 5. Autoregressive Models (Sangjae Lee, Myunghyun Lee)
Youtube: https://youtu.be/j1sdfIgjDLQ
Chapter 6. Normalizing Flow (Kyu Min Khang, Hongjin Kim)
Youtube: https://youtu.be/94WlFyf3DFg
Chapter 7. Energy based Models (Ji Hyun Kim, Myunghyun Lee)
Youtube: https://youtu.be/lET6yQzIp-g
Chapter 8. Diffusion Model (Kyuchul Lee, Eunkyeong Lee)
Youtube: https://youtu.be/QgiY0HTA2ck
Chapter 9. Transformers (Cheyun Yeo, Suyeong Lim)
Youtube: https://youtu.be/VZRb5gjjHSU
Chapter 10. Advanced GAN (Minjae Cha, Sangjae Lee)
Youtube: https://youtu.be/KhALo8gng2A
Chapter 11. Music Generation (Hongjin Kim, Kyu Min Khang)
Youtube: https://youtu.be/hTf9xo2gK7M
Chapter 12. World Models (Cheyun Yeo, Ji Hyun Kim)
Youtube: https://youtu.be/ssVTxiaiyaQ
Chapter 13. Multimodal Models (Sangjae Lee, Kyuchul Lee)
Youtube: https://youtu.be/A03XUGOywC8
Chapter 1. Introducing Deep Learning and Pytorch Library (Presenter: Jae Young Kim)
Youtube: https://youtu.be/V6It47iMu_g
Chapter 2. Pretrained Networks (Presenter: Hyung Won Kim)
Youtube: https://youtu.be/sdeAc64GFeE
Chapter 3. It Starts with a Tensor (Presenter: Shin Hoon Kang)
Youtube: https://youtu.be/mIl94_1wols
Chapter 4. Real-world Data Representation using Tensor (Presenter: Do Young Lee)
Youtube: https://youtu.be/r51VOwHXYGk
Chapter 5. The Mechanics of Learning (Presenter: Doo Jin Lee)
Youtube: https://youtu.be/QlKulpTmdII
Chapter 6. Using a Neural Network to Fit the Data (Presenter: Jae Young Kim)
Youtube: https://youtu.be/RNOopxKiXZc
Chapter 7. Telling Birds from Airplanes (Presenter: Hyung Won Kim)
Youtube: https://youtu.be/KR10ZLKnohg
Chapter 8. Using Convolutions to Generalize (Presenter: Shin Hoon Kang)
Youtube: https://youtu.be/1vGOoO-9F50
Chapter 9. Using Pytorch to Fight Cancers (Presenter: Doo Jin Lee)
Yourtube: https://youtu.be/YI3XHz7XG4k
Chapter 10. Combining Data Sources into an Unified Dataset (Presenter: Jae Young Kim)
Yourtube: https://youtu.be/cMda96QT3aM
Chapter 11. Training a Classification Model to Detect Suspected Tumors (Presenter: Hyung Won Kim)
Youtube: https://youtu.be/saawSCwmml8
Chapter 12. Improving Training with Metrics and Augmentation (Presenter: Shin Hoon Kang)
Youtube: https://youtu.be/vxeJgY2xuHY
Chapter 13. Using Segmentation to Find Suspected Nodules (Presenter: Doo Jin Lee)
Youtube: https://youtu.be/Ceo2HeqbV5w
Chapter 14. End-to-End Nodule Analysis, and Where to Go Next (Presenter: Jae Young Kim)
Youtube: https://youtu.be/X8pwqyS0Vxs
Basic Programming Skills
Programming Tools for Machine Learning Research
Docker and SSH (Sangjae Lee, Minjae Cha)
Youtube: https://youtu.be/fQyYwy43TrQ
VSCode and WSL (Kyumin Khang, Sangjae Lee)
Youtube: https://youtu.be/-tK17eGbsXs
Study Resources
Large Language Model (LLM)
(Book) Build a Large Language Model (From Scratch), Sebastian Raschka.
(Book) Hands-On Large Language Models: Language Understanding and Generation, Jay Alammar, Maarten Grootendorst.
(Book) Prompt Engineering for LLMs: The Art and Science of Building Large Language Model–Based Applications, John Berryman, Albert Ziegler.
(Youtube) Intro to Large Language Models, Andrej Karpathy
(Youtube) Deep Dive into LLMs like ChatGPT, Andrej Karpathy
Deep Learning and Machine Learning
(Book) Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
(Book) The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
(Book) Deep Learning with Python, François Chollet.
(Book) Deep Learning with Pytorch, Eli Stevens, Luca Antiga, and Thomas Viehmann.
(Book) Hands on Machine Learning, Aurélien Géron.
(Book) Interpretable Machine Learning, Christoph Molnar.
(Book) Natural Language Processing with Transformers, Lewis Tunstall, Leandro von Werra, and Thomas Wolf.
(Book) Statistical Rethinking: A Bayesian Course with Examples in R and STAN, Richard McElreath.
(Library) Ray Data, Train, Tune.
(Youtube) Convolutional Neural Networks for Visual Recognition, Stanford CS231n.
(Youtube) Natural Language Processing with Deep Learning, Stanford CS224n.
(Youtube) Statistical Rethinking, Richard McElreath.
Convex Optimization
(Book) Convex Optimization, Stephen Boyd and Lieven Vandenberghe.
(Youtube) Convex Optimization, Stanford.
(Library) CVX (Matlab), CVXPY (Python).
Physics Informed Machine Learning
(Youtube) Physics Informed Machine Learning, Steve Brunton
(Youtube) Topics in Dynamical Systems, Steve Brunton
(Youtube) Engineering Math: Differential Equations and Dynamical Systems, Steve Brunton
(Youtube) PySINDY Tutorial, Alan Kaptanoglu
(Book) Kalman-and-Bayesian-Filters-in-Python
Control Theory, Model Predictive Control
(Youtube) Understanding Model Predictive Control, MATLAB
(Youtube) Understanding PID Control, MATLAB
(Youtube) Control Bootcamp, Steve Brunton
(Library) Control System Toolbox, MATLAB
Reinforcement Learning
(Book) Reinforcement Learning, Richard Sutton and Andrew Barto.
(Book) Grokking Deep Reinforcement Learning, Miguel Morales.
(Book) Deep Reinforcement Learning Hands-on, Maxim Lapan.
(Youtube) Reinforcement Learning, David Silver.
(Youtube) Reinforcement Learning, Steve Brunton
(Library) Ray RLlib: Industry-Grade Reinforcement Learning.
(Library) TorchRL: Reinforcement learning library with pytorch.
Path Planning for Mobile Robot and Autonomous Driving
(Youtube) Mobile Robot Systems Course, University of Cambridge.
(Library) Vectorized Multi Agent Simulator (VMAS).
(Library) Multi-Agent path planning in Python.
(Library) Matlab Navigation Toolbox, Automated Driving Toolbox
(Library) CARLA: Open-source simulator for autonomous driving research.
(Book) Elements of Robotics
Datasets
KAMP-AI Manufacturing Industry Dataset (Manufacturing Dataset)
AIHub Data Repository (General AI real-world Problem)
UCI Machine Learning Data Repository (General Benchmark)
WM-811K Wafer Map Pattern Dataset (Semiconductor Wafer Bin Map)
Berkeley Deep Drive Dataset (Autonomous Driving)
DAGM 2007 Competition Dataset (Anomaly Detection)
The mvtec anomaly detection dataset (Anomaly Detection for Industry)
Programming
(Youtube) 생활코딩 Docker 입구 수업.
(Youtube) 생활코딩 SSH 사용법
(Youtube) 생활코딩 Visual Studio Code 기본 사용법