Search Results for author:
Found 15 papers, 9 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.
Rethinking Propagation for Unsupervised Graph Domain Adaptation
Motivated by our empirical analysis, we reevaluate the role of GNNs in graph domain adaptation and uncover the pivotal role of the propagation process in GNNs for adapting to different graph domains.
State Value Generation with Prompt Learning and Self-Training for Low-Resource Dialogue State Tracking
Experimental results on the MultiWOZ 2. 1 dataset show that our method which has only less than 1 billion parameters achieves state-of-the-art performance under the data ratio settings of 5%, 10%, and 25% when limited to models under 100 billion parameters.
GridFormer: Point-Grid Transformer for Surface Reconstruction
Our method maximizes the spatial expressiveness of grid features and maintains computational efficiency.
NeuSurf: On-Surface Priors for Neural Surface Reconstruction from Sparse Input Views
To achieve this, we train a neural network to learn a global implicit field from the on-surface points obtained from SfM and then leverage it as a coarse geometric constraint.
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.
document understanding
Optical Character Recognition (OCR)
+1
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.
