Search Results for author:
Found 25 papers, 18 papers with code
Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond
In this paper, we present a comprehensive review of the development and recent advances in deep learning for trajectory computing (DL4Traj).
FIT-RAG: Black-Box RAG with Factual Information and Token Reduction
Simply concatenating all the retrieved documents brings large amounts of unnecessary tokens for LLMs, which degenerates the efficiency of black-box RAG.
FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis
Text-to-SQL, which provides zero-code interface for operating relational databases, has gained much attention in financial analysis; because, financial professionals may not well-skilled in SQL programming.
Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment
In this paper, we present Starling, an I/O-efficient disk-resident graph index framework that optimizes data layout and search strategy within the segment.
View-based Explanations for Graph Neural Networks
Existing approaches aim to understand the overall results of GNNs rather than providing explanations for specific class labels of interest, and may return explanation structures that are hard to access, nor directly queryable. We propose GVEX, a novel paradigm that generates Graph Views for EXplanation.
MUST: An Effective and Scalable Framework for Multimodal Search of Target Modality
We investigate the problem of multimodal search of target modality, where the task involves enhancing a query in a specific target modality by integrating information from auxiliary modalities.
MultiEM: Efficient and Effective Unsupervised Multi-Table Entity Matching
Traditional unsupervised EM assumes that all entities come from two tables; however, it is more common to match entities from multiple tables in practical applications, that is, multi-table entity matching (multi-table EM).
C3: Zero-shot Text-to-SQL with ChatGPT
This paper proposes a ChatGPT-based zero-shot Text-to-SQL method, dubbed C3, which achieves 82. 3\% in terms of execution accuracy on the holdout test set of Spider and becomes the state-of-the-art zero-shot Text-to-SQL method on the Spider Challenge.
Ranked #4 on
Text-To-SQL
on spider
Real-time Workload Pattern Analysis for Large-scale Cloud Databases
This has led to the increasing volume of database workloads, which provides the opportunity for pattern analysis.
Knowledge-refined Denoising Network for Robust Recommendation
Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability.
Towards Explainable Collaborative Filtering with Taste Clusters Learning
In recent decades, there have been significant advancements in latent embedding-based CF methods for improved accuracy, such as matrix factorization, neural collaborative filtering, and LightGCN.
SEA: A Scalable Entity Alignment System
To enhance the usability of GNN-based EA models in real-world applications, we present SEA, a scalable entity alignment system that enables to (i) train large-scale GNNs for EA, (ii) speed up the normalization and the evaluation process, and (iii) report clear results for users to estimate different models and parameter settings.
SparDL: Distributed Deep Learning Training with Efficient Sparse Communication
Recently, a few methods have been put forward to handle the SGA dilemma.
Unsupervised Entity Alignment for Temporal Knowledge Graphs
State-of-the-art time-aware EA studies have suggested that the temporal information of TKGs facilitates the performance of EA.
Ranked #1 on
Entity Alignment
on YAGO-WIKI50K
Estimator: An Effective and Scalable Framework for Transportation Mode Classification over Trajectories
Transportation mode classification, the process of predicting the class labels of moving objects transportation modes, has been widely applied to a variety of real world applications, such as traffic management, urban computing, and behavior study.
ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities
To tackle this challenge, we present ClusterEA, a general framework that is capable of scaling up EA models and enhancing their results by leveraging normalization methods on mini-batches with a high entity equivalent rate.
Ranked #2 on
Entity Alignment
on DBP1M DE-EN
Self-Guided Learning to Denoise for Robust Recommendation
Besides, our method can automatically switch its learning phase at the memorization point from memorization to self-guided learning, and select clean and informative memorized data via a novel adaptive denoising scheduler to improve the robustness.
HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation
Furthermore, we propose a dual item embeddings design to represent and propagate collaborative signals and knowledge associations separately, and leverage the gated aggregation to distill discriminative information for better capturing user behavior patterns.
Ranked #1 on
Recommendation Systems
on Alibaba-iFashion
Temporal Feature Alignment and Mutual Information Maximization for Video-Based Human Pose Estimation
State-of-the-art methods strive to incorporate additional visual evidences from neighboring frames (supporting frames) to facilitate the pose estimation of the current frame (key frame).
MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation
Inspired by the success of meta-learning on scarce training samples, we propose a novel meta-learning based framework called MetaKG, which encompasses a collaborative-aware meta learner and a knowledge-aware meta learner, to capture meta users' preference and entities' knowledge for cold-start recommendations.
Deep Spatially and Temporally Aware Similarity Computation for Road Network Constrained Trajectories
Trajectory similarity computation has drawn massive attention, as it is core functionality in a wide range of applications such as ride-sharing, traffic analysis, and social recommendation.
FastSGD: A Fast Compressed SGD Framework for Distributed Machine Learning
Distributed ML models trained by SGD involve large amounts of gradient communication, which limits the scalability of distributed ML.
Finding Materialized Models for Model Reuse
It uses a Gaussian mixture-based metric called separation degree to rank materialized models.
Generative Semi-supervised Learning for Multivariate Time Series Imputation
In this paper, we propose a novel semi-supervised generative adversarial network model, named SSGAN, for missing value imputation in multivariate time series data.
Generative Adversarial Network
Multivariate Time Series Imputation
+2
CrowdTSC: Crowd-based Neural Networks for Text Sentiment Classification
We propose Crowd-based neural networks for Text Sentiment Classification (CrowdTSC for short).
