STUDY

Journals to read

• Journal list

List of journals 

• Skeleton Action Recognition

  • Skeleton-Based Action Recognition with Directed Graph Neural Networks 

  • OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

  • A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

  • Feature Extraction Using an RNN Autoencoder for Skeleton-Based Abnormal Gait Recognition

  • Spatial-Temporal Data Augmentation Based on LSTM Autoencoder Network for Skeleton-Based Human Action Recognition

  • Image-based OoD Detector Principles on Graph-based Input Data in Human Action Recognition

• Graph Neural Network

  • Bidirectional Recurrent Autoencoder for 3D Skeleton Motion Data Refinement

  • Neural Relational Inference for Interacting Systems

  • Hamiltonian Graph Networks with ODE Integrators

  • Relational Inductive Biases, Deep Learning, and Graph Networks

  • A Graph-Convolutional Neural Network Model for the Prediction of Chemical Reactivity

  • Graph Networks as Learnable Physics Engines for Inference and Control

  • Design Space for Graph Neural Networks

  • Learning to Simulate Complex Physics with Graph Networks

  • Inductive Representation Learning on Large Graphs

  • Semantic Graph Convolutional Networks for 3D Human Pose Regression

• Operator Learning

  • Neural Fields in Visual Computing and Beyond

• PINN

  • Physics-Informed Neural Networks: A Deep Learning Framework for Solving forward and Inverse Problems Involving Nonlinear Partial Differential Equations

  • Physics-Informed Graph Learning: A Survey

  • Physics-Informed Graph Neural Galerkin Networks: A Unified Framework for Solving PDE-governed Forward and Inverse Problems

  • DeepXDE: A Deep Learning Library for Solving Differential Equations

  • Differentiable Physics-informed Graph Networks

  • Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

  • PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network

  • Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data

• Extra

  • Attention Is All You Need

  • Deep Symbolic Regression: Recovering Mathematical Expressions from Data via Risk-Seeking Policy Gradients

  • Discovering Symbolic Models from Deep Learning with Inductive Biases

  • SiT: Simulation Transformer for Particle-Based Physics Simulation

  • Hamiltonian Neural Networks

  • Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately