To validate our viewpoints, we design two methods to evaluate the robustness of FMS: (1) model disguise attack, which post-trains an inferior PTM with a contrastive objective, and (2) evaluation data selection, which selects a subset of the data points for FMS evaluation based on K-means clustering.
Recently, various multimodal networks for Visually-Rich Document Understanding(VRDU) have been proposed, showing the promotion of transformers by integrating visual and layout information with the text embeddings.
For $m\times n$ matrices where a few principal components explain most of the variance in the data, we develop one such algorithm that runs in $O(mnl)$ time, where $l\ll \min(m, n)$ is a small multiple of the number of principal components.
In contrast, randomized QRCP (RQRCP) algorithms have proven themselves empirically to be highly competitive with high-performance implementations of QR in processing time, on uniprocessor and shared memory machines, and as reliable as QRCP in pivot quality.
We present Flip-Flop Spectrum-Revealing QR (Flip-Flop SRQR) factorization, a significantly faster and more reliable variant of the QLP factorization of Stewart, for low-rank matrix approximations.
Numerical Analysis Numerical Analysis 15A18, 15A23, 65F99
In this paper, we propose a new algorithm for graph partitioning with an objective function that follows the min-max clustering principle.