Search Results for author:
Found 16 papers, 6 papers with code
Best Practices and Lessons Learned on Synthetic Data for Language Models
The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and high costs.
Higher Layers Need More LoRA Experts
In this paper, we introduce a novel parameter-efficient MoE method, \textit{\textbf{M}oE-L\textbf{o}RA with \textbf{L}ayer-wise Expert \textbf{A}llocation (MoLA)} for Transformer-based models, where each model layer has the flexibility to employ a varying number of LoRA experts.
Here Is Not There: Measuring Entailment-Based Trajectory Similarity for Location-Privacy Protection and Beyond
In this work, we present a different view on trajectory similarity by introducing a measure that utilizes logical entailment.
FLEE-GNN: A Federated Learning System for Edge-Enhanced Graph Neural Network in Analyzing Geospatial Resilience of Multicommodity Food Flows
This paper proposes FLEE-GNN, a novel Federated Learning System for Edge-Enhanced Graph Neural Network, designed to overcome these challenges and enhance the analysis of geospatial resilience of multicommodity food flow network, which is one type of spatial networks.
SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution
Existing digital sensors capture images at fixed spatial and spectral resolutions (e. g., RGB, multispectral, and hyperspectral images), and each combination requires bespoke machine learning models.
Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models
Through this vision paper, we hope to draw the attention of researchers and policymakers in geospatial domains to these privacy and security risks inherent in GeoAI foundation models and advocate for the development of privacy-preserving and secure GeoAI foundation models.
CATS: Conditional Adversarial Trajectory Synthesis for Privacy-Preserving Trajectory Data Publication Using Deep Learning Approaches
The prevalence of ubiquitous location-aware devices and mobile Internet enables us to collect massive individual-level trajectory dataset from users.
Evaluation and Enhancement of Semantic Grounding in Large Vision-Language Models
Large Vision-Language Models (LVLMs) offer remarkable benefits for a variety of vision-language tasks.
Tackling Vision Language Tasks Through Learning Inner Monologues
Visual language tasks require AI models to comprehend and reason with both visual and textual content.
Measuring Network Resilience via Geospatial Knowledge Graph: a Case Study of the US Multi-Commodity Flow Network
Quantifying the resilience in the food system is important for food security issues.
STICC: A multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity
In this paper, we propose a Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both attributes and spatial relationships of geographic objects for multivariate spatial clustering.
Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic
Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for monitoring and measuring the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the pandemic.
Social and Information Networks Physics and Society
LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection
The prevalence of location-based services contributes to the explosive growth of individual-level trajectory data and raises public concerns about privacy issues.
Mobile phone location data reveal the effect and geographic variation of social distancing on the spread of the COVID-19 epidemic
The emergence of SARS-CoV-2 and the coronavirus infectious disease (COVID-19) has become a pandemic.
Social and Information Networks Physics and Society Populations and Evolution 65D10 H.4; G.3; J.2
Mapping county-level mobility pattern changes in the United States in response to COVID-19
To contain the Coronavirus disease (COVID-19) pandemic, one of the non-pharmacological epidemic control measures in response to the COVID-19 outbreak is reducing the transmission rate of SARS-COV-2 in the population through (physical) social distancing.
Physics and Society Social and Information Networks Populations and Evolution H.4; H.5
