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Machine Learning, Deep Learning, Data Science, Data Engineering, Statistics..
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Recent Publications (First author)
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<br> - [***NeurIPSW 2024 Oral***] Partial Channel Dependence with Channel Masks for Time Series Foundation Models <br> - [***NeurIPSW 2024***] Sequential Order-Robust Mamba for Time Series Forecasting <br> - [***NeurIPS 2024***] ANT: Adaptive Noise Schedule for Time Series Diffusion Models <br> - [***ICLR 2024 Spotlight***] Soft Contrastive Learning for Time Series <br> - [***ICLR 2024, NeurIPSW 2023 Oral***] Learning to Embed Time Series Patches Independently <br> <br> <br>
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PRML
Summary of Chapter 01~12
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Machine Learning
About Various ML Algorithms
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DL Framework
Pytorch, Tensorflow2
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Time Series 1
TS basic
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Time Series 2
TS paper reviews
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Diffusion Models
Diffusion/Score-based models
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MAMBA
Selective SSM
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Audio DL
DL with Audio Data
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Tabular DL
DL with Tabular Data
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Paper Reading Study
2021.05 ~ ing
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Git
Git, Github
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SQL
sql
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Advanced Python
Advanced Python Coding Skills
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Fluent Python
Fluent Python by O'Reilly
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Docker
Docker, linux (ubuntu, centos)
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Kubernetes
Kubernetes
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Computer Science
Javascript,Linux,HTML,CSS..
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MLOps
Machine Learning Operations
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Java
Java
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R programming
R programming
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Operating Systems
OS
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Computer Vision
About Various CV Algorithms
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NLP
Natural Language Processing
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GAN
Generative Adversarial Network
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RL
Reinforcement Learning
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Rec Sys
Recommender System
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Self-SL
Self-SL & Contrastive learning
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Semi-SL
Semi-SL
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Convex Optimization
convex optimization
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Graph NN
CS224W, Deep Walk, LINE, node2vec...
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Domain Adaptation
Tranfer Learning, Domain Adaptation
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Bayesian NN & VI
Paper reviews & Tensorflow2
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Meta Learning
Paper Reviews & CS330
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Continual Learning
Paper Reviews
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Reliable DL
Reliable Deep Learning
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Interpretable DL
Interpretable Deep Learning
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Multimodal DL
Multimodal Deep Learning
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Hierarchical BERT
Hierarchical BERT & ABSA
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ABSA
Aspect based Sentiment Analysis
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Bayesian Statistics
Gibbs Sampling & VI
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Data Engineering
Basics of Data Engineering
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Presentation
Presentations & Lecture Notes
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Projects
Projects & Competition
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Seunghan Lee
(B.S) Yonsei Univ., Business Administration/Applied Statistics <br> (M.S, Ph.D) Yonsei Univ., Statistics and Data Science <br> <br> **Research Topics** <br> - Time Series (TS) Deep Learning <br> - TS Forecasting <br> - TS Representation Learning <br> - TS Diffusion Models, Foundation Models <br> <br>T. 010-8768-8472 <br> E. [email protected]
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