So sánh phương pháp

	Mô hình sinh sinh theo điểm số	Neural ODE
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2019	2018
Người khởi xướng	Song, Y. & Ermon, S.	Chen, T. Q. et al.
Loại	Score-based generative model (SDE framework)	Continuous-depth neural network (ODE-parameterised dynamics)
Công trình gốc	Song, Y. & Ermon, S. (2019). Generative Modeling by Estimating Gradients of the Data Distribution. NeurIPS 32, 11895–11907.	Chen, T. Q., Rubanova, Y., Bettencourt, J. & Duvenaud, D. (2018). Neural Ordinary Differential Equations. Advances in Neural Information Processing Systems (NeurIPS).
Tên gọi khác	Skor Tabanlı Üretici Model (Score-Based / SDE), score-based diffusion, SDE-based generative model, score SDE	Nöral Diferansiyel Denklem (Neural ODE), neural ordinary differential equation, continuous-depth network, ODE-Net
Liên quan	5	4
Tóm tắt	A score-based generative model, introduced by Yang Song and Stefano Ermon in 2019 and generalized to the stochastic differential equation (SDE) framework in 2021, learns the gradient of the data density — the score — rather than predicting noise directly, and uses it to generate new samples. It is the mathematical generalization that unifies diffusion models under a continuous-time formulation.	A Neural ODE, introduced by Chen and colleagues in 2018, models a hidden state as the continuous solution of an ordinary differential equation whose dynamics are parameterised by a neural network. It generalises the limiting case of residual connections, making it well suited to irregularly spaced time series and physics-based modelling.