Introduction to Geometric Deep Learning Certificate

  • Recruiting People999 people

  • Learning period03-01-2024 ~ 12-31-2024

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Class Introduction

강의시간

강의내용

실습여부

1

-  Why CNNs, MLPs, RNNs are insufficient for non-Euclidean data

-  Neural network for graphs & sets

-  Euclidean transformations, invariance, and equivariance

 

2

-  Invariant geometric GNNs (SchNet, DimeNet, and SphereNet)

-  Simple equivariant geometric GNNs (EGNN and NequIP)

O

3

-  Local frame-based geometric GNNs (ClofNet and LEFTNet)

-  Frame averaging for geometric GNNs (Frame averaging, FAENet)

O

4

-  Steerable features, rreducible representations, Wigner-D matrix, spherical harmonics, Clebsch-Gordan tensor product

-  Steerable geometric GNNs (Tensor field network, SE(3)-Transformer)

 

5

- Steerable geometric GNNs (Equiformer, MACE, eSCN, EquifomerV2)

O
Course Introduction

강의 소개 및 개요입니다.

성명

안성수

소속기관

POSTECH

과목명

Introduction       to          Geometric              Deep Learning

 강의시간

5

학습목표

Geometric deep learning (GDL) aims to develop models capable of handling structured and non-Euclidean data, such as geometric graphs. In this course, we study recent GDL models, with the focus on geometric graph neural networks for molecules.

 

강의 선수과목 및 준비사항입니다.

선수과목

graph neural network

참고자료

 

준비사항

실습에 참여할 경우 실험을,  위한 GPU 세팅
Classification
  • Subject
    Machine Learning
  • Class Area
    AI & Programming
  • Practice
    Programming (Python)
  • Utilize
    Drug Discovery
Professor
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