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Found 18 papers, 5 papers with code
Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering
In the context of multi-view clustering, graph learning is recognized as a crucial technique, which generally involves constructing an adaptive neighbor graph based on probabilistic neighbors, and then learning a consensus graph to for clustering.
Nonlinear subspace clustering by functional link neural networks
Additionally, we introduce a convex combination subspace clustering scheme, which combining a linear subspace clustering method with the functional link neural network subspace clustering approach.
Enhanced Latent Multi-view Subspace Clustering
Specifically, we stack the data matrices from various views into the block-diagonal locations of the augmented matrix to exploit the complementary information.
Extract-Transform-Load for Video Streams
We find that no current system sufficiently fulfills both needs and therefore propose Skyscraper, a system tailored to V-ETL.
SEED: Domain-Specific Data Curation With Large Language Models
As a result, data scientists often have to develop domain-specific solutions tailored to both the dataset and the task, e. g. writing domain-specific code or training machine learning models on a sufficient number of annotated examples.
VerifAI: Verified Generative AI
We propose that verifying the outputs of generative AI from a data management perspective is an emerging issue for generative AI.
RoTaR: Efficient Row-Based Table Representation Learning via Teacher-Student Training
In addition to the row-based architecture, we introduce several techniques: cell-aware position embedding, teacher-student training paradigm, and selective backward to improve the performance of RoTaR model.
Lingua Manga: A Generic Large Language Model Centric System for Data Curation
Data curation is a wide-ranging area which contains many critical but time-consuming data processing tasks.
Interleaving Pre-Trained Language Models and Large Language Models for Zero-Shot NL2SQL Generation
PLMs can perform well in schema alignment but struggle to achieve complex reasoning, while LLMs is superior in complex reasoning tasks but cannot achieve precise schema alignment.
RITA: Group Attention is All You Need for Timeseries Analytics
Timeseries analytics is of great importance in many real-world applications.
Interpretable Outlier Summarization
Moreover, to effectively handle high dimensional, highly complex data sets which are hard to summarize with simple rules, we propose a localized STAIR approach, called L-STAIR.
Scalable Motif Counting for Large-scale Temporal Graphs
We first categorize the temporal motifs based on their distinct properties, and then design customized algorithms that offer efficient strategies to exactly count the motif instances of each category.
Parallel Fourier Ptychography reconstruction
Fourier ptychography has attracted a wide range of focus for its ability of large space-bandwidth-produce, and quantative phase measurement.
Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media
As the most popular platform for self-expression, emotion release, and personal interaction, individuals may exhibit a number of symptoms of suicidal ideation on social media.
Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention
Despite detection of suicidal ideation on social media has made great progress in recent years, people's implicitly and anti-real contrarily expressed posts still remain as an obstacle, constraining the detectors to acquire higher satisfactory performance.
Context-Aware Object Detection With Convolutional Neural Networks
Although the state-of-the-art object detection methods are successful in detecting and classifying objects by leveraging deep convolutional neural networks (CNNs), these methods overlook the semantic context which implies the probabilities that different classes of objects occur jointly.
Unknown-Aware Deep Neural Network
Unfortunately, although the strong generalization ability of existing CNNs ensures their accuracy when classifying known objects, it also causes them to often assign an unknown to a target class with high confidence.
Outlier Detection from Image Data
Modern applications from Autonomous Vehicles to Video Surveillance generate massive amounts of image data.
