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Found 5 papers, 2 papers with code
Is your data alignable? Principled and interpretable alignability testing and integration of single-cell data
To overcome these limitations, we present a spectral manifold alignment and inference (SMAI) framework, which enables principled and interpretable alignability testing and structure-preserving integration of single-cell data with the same type of features.
Convex Sparse Blind Deconvolution
To bridge the gulf between reported successes and theory's limited understanding, we exhibit a convex optimization problem that -- assuming signal sparsity -- can convert a crude approximation to the true filter into a high-accuracy recovery of the true filter.
Degrees of Freedom Analysis of Unrolled Neural Networks
Leveraging the Stein's Unbiased Risk Estimator (SURE), this paper analyzes the generalization risk with its bias and variance components for recurrent unrolled networks.
Neural Proximal Gradient Descent for Compressive Imaging
Recovering high-resolution images from limited sensory data typically leads to a serious ill-posed inverse problem, demanding inversion algorithms that effectively capture the prior information.
Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery
Building effective priors is however challenged by the low train and test overhead dictated by real-time tasks; and the need for retrieving visually "plausible" and physically "feasible" images with minimal hallucination.
