Search Results for author:
Found 11 papers, 6 papers with code
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained Models
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.
Homophily-enhanced Structure Learning for Graph Clustering
Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results.
A Geometric-Relational Deep Learning Framework for BIM Object Classification
It can be supplemented using deep learning.
A2: Efficient Automated Attacker for Boosting Adversarial Training
In this paper, we propose an efficient automated attacker called A2 to boost AT by generating the optimal perturbations on-the-fly during training.
XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich Document Understanding
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.
Modeling and Validating Temporal Rules with Semantic Petri-Net for Digital Twins
Semantic rule checking on RDFS/OWL data has been widely used in the construction industry.
An Efficient and Reliable Tolerance-Based Algorithm for Principal Component Analysis
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.
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data
Low-rank metric learning aims to learn better discrimination of data subject to low-rank constraints.
Fast Parallel Randomized QR with Column Pivoting Algorithms for Reliable Low-rank Matrix Approximations
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.
Numerical Analysis
Low-Rank Matrix Approximations with Flip-Flop Spectrum-Revealing QR Factorization
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
A Min-max Cult Algorithm for Graph Partitioning and Data Clustering
In this paper, we propose a new algorithm for graph partitioning with an objective function that follows the min-max clustering principle.
