Using deep neural networks to solve PDEs has attracted a lot of attentions recently. However,
why the deep learning method works is falling far behind its empirical success. In this …

Remote sensing images are often contaminated by varying degrees of stripes, which severely
affects the visual quality and subsequent application of the data. Unlike with conventional …

We propose a constructive approach to estimating sparse, high-dimensional linear regression
models. The approach is a computational algorithm motivated from the KKT conditions for …

We develop a primal dual active set with continuation algorithm for solving the ℓ 0 -regularized
least-squares problem that frequently arises in compressed sensing. The algorithm …

Recent research shows that pre-trained language models (PLMs) suffer from "prompt bias"
in factual knowledge extraction, ie, prompts tend to introduce biases toward specific labels. …

In recent years, physical informed neural networks (PINNs) have been shown to be a powerful
tool for solving PDEs empirically. However, numerical analysis of PINNs is still missing. In …

Striping effects are a common phenomenon in remote-sensing imaging systems, and they
can exhibit considerable differences between different sensors. Such artifacts can greatly …

Fuzzy clustering generates a membership vector for each data point in the dataset to indicate
its belongingness to different clusters. This procedure can be regarded as an encoding …

In this paper we propose an iterative method using alternating direction method of multipliers
(ADMM) strategy to solve linear inverse problems in Hilbert spaces with a general convex …

A natural image u is often sparse under a given transformation W, one can use $$L_0$$ L 0
norm of Wu as a regularisation term in image reconstructions. Since minimizing the $$L_0$…