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Found 20 papers, 12 papers with code
Non-linear Attributed Graph Clustering by Symmetric NMF with PU Learning
We consider the clustering problem of attributed graphs.
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach
The feature extraction model transforms original data and threshold to a Hamming space, in which a deep learning-based regression model is utilized to exploit the incremental property of cardinality w. r. t.
Fast Subgraph Matching by Exploiting Search Failures
That is, our algorithm generates failure patterns when a partial embedding is found unable to become an isomorphic embedding.
Databases
Edit Distance Based Curriculum Learning for Paraphrase Generation
Curriculum learning has improved the quality of neural machine translation, where only source-side features are considered in the metrics to determine the difficulty of translation.
AIREX: Neural Network-based Approach for Air Quality Inference in Unmonitored Cities
Since existing methods aim to infer air quality of locations only in monitored cities, they do not assume inferring air quality in unmonitored cities.
FedMe: Federated Learning via Model Exchange
First, to optimize the model architectures for local data, clients tune their own personalized models by comparing to exchanged models and picking the one that yields the best performance.
Similarity Search on Computational Notebooks
In this paper, we propose a similarity search on computational notebooks and develop a new framework for the similarity search.
Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs
We hope this work offers interesting insights for future research.
Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander
Nowadays, so as to improve services and urban areas livability, multiple smart city initiatives are being carried out throughout the world.
An Empirical Study of Personalized Federated Learning
Federated learning is a distributed machine learning approach in which a single server and multiple clients collaboratively build machine learning models without sharing datasets on clients.
Scaling Private Deep Learning with Low-Rank and Sparse Gradients
Applying Differentially Private Stochastic Gradient Descent (DPSGD) to training modern, large-scale neural networks such as transformer-based models is a challenging task, as the magnitude of noise added to the gradients at each iteration scales with model dimension, hindering the learning capability significantly.
GNN Transformation Framework for Improving Efficiency and Scalability
We propose a framework that automatically transforms non-scalable GNNs into precomputation-based GNNs which are efficient and scalable for large-scale graphs.
Scardina: Scalable Join Cardinality Estimation by Multiple Density Estimators
In recent years, machine learning-based cardinality estimation methods are replacing traditional methods.
Learned spatial data partitioning
Due to the significant increase in the size of spatial data, it is essential to use distributed parallel processing systems to efficiently analyze spatial data.
GuP: Fast Subgraph Matching by Guard-based Pruning
Subgraph matching, which finds subgraphs isomorphic to a query, is the key to information retrieval from data represented as a graph.
A Simple and Scalable Graph Neural Network for Large Directed Graphs
In response, we propose a simple yet holistic classification method A2DUG which leverages all combinations of node representations in directed and undirected graphs.
Ranked #1 on
Node Classification
on wiki
BClean: A Bayesian Data Cleaning System
By evaluating on both real-world and synthetic datasets, we demonstrate that BClean is capable of achieving an F-measure of up to 0. 9 in data cleaning, outperforming existing Bayesian methods by 2% and other data cleaning methods by 15%.
Shall We Talk: Exploring Spontaneous Collaborations of Competing LLM Agents
Recent advancements have shown that agents powered by large language models (LLMs) possess capabilities to simulate human behaviors and societal dynamics.
Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation
The key technical contribution is a novel LLM agent framework that accounts for individual activity patterns and motivations, including a self-consistency approach to align LLMs with real-world activity data and a retrieval-augmented strategy for interpretable activity generation.
Language-agnostic Representation from Multilingual Sentence Encoders for Cross-lingual Similarity Estimation
Experimental results on both quality estimation of machine translation and cross-lingual semantic textual similarity tasks reveal that our method consistently outperforms the strong baselines using the original multilingual embedding.
Cross-Lingual Semantic Textual Similarity
Machine Translation
+3
