9 articles
Boltzmann generator
A Boltzmann generator is a machine learning method introduced by Frank Noé and colleagues in 2019 that samples equilibrium states of many-body systems with deep learning.
BYOL
BYOL (Bootstrap Your Own Latent) is a self-supervised algorithm introduced at DeepMind in a 2020 NeurIPS paper that learns image representations without labels, using no negative pairs.
Chemical language model
A chemical language model (CLM) is a neural sequence model that treats molecular structures as text strings, used in drug discovery for property prediction, virtual screening, and molecule generation.
Denoising autoencoder
A denoising autoencoder (DAE) is an autoencoder trained to reconstruct clean data from corrupted input, introduced by Vincent, Larochelle, Bengio, and Manzagol at ICML 2008.
Equivariant Diffusion for Molecule Generation in 3D
Equivariant diffusion for molecule generation in 3D is a family of deep generative methods that produce new 3D molecular structures, founded by the EDM model in 2022.
GFlowNet
GFlowNet, or Generative Flow Network, is a family of generative models introduced in 2021 by Emmanuel Bengio and Yoshua Bengio that samples objects such as molecular graphs in proportion to a reward function.
GraphAF
GraphAF is a deep generative model that builds molecules one atom at a time on a graph, proposed by Chence Shi and colleagues in 2020 and published at ICLR 2020.
MoCo (machine learning)
MoCo (Momentum Contrast) is a contrastive self-supervised learning framework for visual representations, introduced by Kaiming He and colleagues at CVPR 2020, using a momentum-updated encoder and a queue of keys.
Normalizing flows
Normalizing flows are generative models that transform a simple base distribution, typically a Gaussian, into a complex target distribution through invertible transformations, enabling exact density evaluation and efficient sampling.