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Found 3 papers, 2 papers with code
Sequential Convolution and Runge-Kutta Residual Architecture for Image Compressed Sensing
In recent years, Deep Neural Networks (DNN) have empowered Compressed Sensing (CS) substantially and have achieved high reconstruction quality and speed far exceeding traditional CS methods.
Ranked #1 on
Image Compression
on BSDS500
Learning Robust Statistics for Simulation-based Inference under Model Misspecification
Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics to infer parameters of intractable likelihood models.
Bayesian Optimization Augmented with Actively Elicited Expert Knowledge
We design a multi-task learning architecture for this task, with the goal of jointly eliciting the expert knowledge and minimizing the objective function.
