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Found 23 papers, 8 papers with code
Semantic-Enhanced Representation Learning for Road Networks with Temporal Dynamics
In this study, we introduce a novel framework called Toast for learning general-purpose representations of road networks, along with its advanced counterpart DyToast, designed to enhance the integration of temporal dynamics to boost the performance of various time-sensitive downstream tasks.
A Study of Shape Modeling Against Noise
Shape modeling is a challenging task with many potential applications in computer vision and medical imaging.
AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction
Air quality prediction and modelling plays a pivotal role in public health and environment management, for individuals and authorities to make informed decisions.
Spatial-Temporal Large Language Model for Traffic Prediction
In this paper, we propose a Spatial-Temporal Large Language Model (ST-LLM) for traffic prediction.
KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy
To address this issue, we first present a novel Increment training strategy: instead of masking nodes (and reconstructing them), we add virtual nodes into the training graph so as to mitigate the graph gap issue naturally.
Defense Against Model Extraction Attacks on Recommender Systems
These gradients are used to compute a swap loss, which maximizes the loss of the student model.
Multi-Factor Spatio-Temporal Prediction based on Graph Decomposition Learning
The framework consists of two main components: an automatic graph decomposition module that decomposes the original graph structure inherent in ST data into subgraphs corresponding to different factors, and a decomposed learning network that learns the partial ST data on each subgraph separately and integrates them for the final prediction.
Self-Calibrated Cross Attention Network for Few-Shot Segmentation
Moreover, when calculating SCCA, we design a scaled-cosine mechanism to better utilize the support features for similarity calculation.
Ranked #7 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
OpenSiteRec: An Open Dataset for Site Recommendation
As a representative information retrieval task, site recommendation, which aims at predicting the optimal sites for a brand or an institution to open new branches in an automatic data-driven way, is beneficial and crucial for brand development in modern business.
Road Network Representation Learning: A Dual Graph based Approach
The constructed hypergraph would naturally capture the high-order relationships among roads with hyperedges.
Road Extraction with Satellite Images and Partial Road Maps
In this paper, we propose to conduct road extraction based on satellite images and partial road maps, which is new.
High-Dimensional Approximate Nearest Neighbor Search: with Reliable and Efficient Distance Comparison Operations
Approximate K nearest neighbor (AKNN) search is a fundamental and challenging problem.
Region Embedding with Intra and Inter-View Contrastive Learning
Motivated by the success of contrastive learning for representation learning, we propose to leverage it for multi-view region representation learning and design a model called ReMVC (Region Embedding with Multi-View Contrastive Learning) by following two guidelines: i) comparing a region with others within each view for effective representation extraction and ii) comparing a region with itself across different views for cross-view information sharing.
On Inferring User Socioeconomic Status with Mobility Records
The DeepSEI model incorporates two networks called deep network and recurrent network, which extract the features of the mobility records from three aspects, namely spatiality, temporality and activity, one at a coarse level and the other at a detailed level.
Reinforcement Learning Enhanced Weighted Sampling for Accurate Subgraph Counting on Fully Dynamic Graph Streams
Specifically, we propose a weighted sampling algorithm called WSD for estimating the subgraph count in a fully dynamic graph stream, which samples the edges based on their weights that indicate their importance and reflect their properties.
Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning
Detecting anomalous trajectories has become an important task in many location-based applications.
A Survey on Neural Open Information Extraction: Current Status and Future Directions
Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational facts from large corpora.
Ranked #1 on
Open Information Extraction
on CaRB
Natural Language Understanding
Open-Domain Question Answering
+1
Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks
As a fundamental component in location-based services, inferring the relationship between points-of-interests (POIs) is very critical for service providers to offer good user experience to business owners and customers.
A Reinforcement Learning Based R-Tree for Spatial Data Indexing in Dynamic Environments
Learned indices have been proposed to replace classic index structures like B-Tree with machine learning (ML) models.
Towards advancing the earthquake forecasting by machine learning of satellite data
Amongst the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range.
Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning
Similar trajectory search is a fundamental problem and has been well studied over the past two decades.
Interaction-aware Kalman Neural Networks for Trajectory Prediction
Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.)
Robotics I.2.9; I.2.0
Representation Learning for Spatial Graphs
Recently, the topic of graph representation learning has received plenty of attention.
