Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings
Likelihood-free inference is quickly emerging as a powerful tool to perform fast/effective parameter estimation. We demonstrate a technique of optimizing likelihood-free inference to make it even faster by marginalizing symmetries in a physical problem. In this approach, physical symmetries, for example, time-translation are learned using joint-embedding via self-supervised learning with symmetry data augmentations. Subsequently, parameter inference is performed using a normalizing flow where the embedding network is used to summarize the data before conditioning the parameters. We present this approach on two simple physical problems and we show faster convergence in a smaller number of parameters compared to a normalizing flow that does not use a pre-trained symmetry-informed representation.
Code (1)
Tasks
parameter estimationSelf-Supervised LearningTranslationSimilar Papers 제목 키워드 기반
InfoVAEGAN : learning joint interpretable representations by information maximization and maximum likelihood
Learning disentangled and interpretable representations is an important step towards accomplishing comprehensive data representations on the manifold. In this paper, we propose a novel representation learning algorithm w…
Representation LearningJoint Embedding Variational Bayes
We introduce Variational Joint Embedding (VJE), a reconstruction-free latent-variable framework for non-contrastive self-supervised learning in representation space. VJE maximizes a symmetric conditional evidence lower b…
Out-of-Distribution DetectionSelf-Supervised LearningContrastive LearningWalker: Self-supervised Multiple Object Tracking by Walking on Temporal Appearance Graphs
The supervision of state-of-the-art multiple object tracking (MOT) methods requires enormous annotation efforts to provide bounding boxes for all frames of all videos, and instance IDs to associate them through time. To …
Multiple Object TrackingObject TrackingA General Recipe for Likelihood-free Bayesian Optimization
The acquisition function, a critical component in Bayesian optimization (BO), can often be written as the expectation of a utility function under a surrogate model. However, to ensure that acquisition functions are tract…
Bayesian OptimizationQuantum parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles
We show that ensembles of deep neural networks, called deep ensembles, can be used to perform quantum parameter estimation while also providing a means for quantifying uncertainty in parameter estimates, which is a key a…
Bayesian Inference