Search Results for author:
Found 7 papers, 4 papers with code
LLM-Driven Multi-step Translation from C to Rust using Static Analysis
Translating software written in legacy languages to modern languages, such as C to Rust, has significant benefits in improving memory safety while maintaining high performance.
Geo-Llama: Leveraging LLMs for Human Mobility Trajectory Generation with Spatiotemporal Constraints
Simulating human mobility data is essential for various application domains, including transportation, urban planning, and epidemic control, since real data are often inaccessible to researchers due to expensive costs and privacy issues.
Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data
This paper introduces NeuroGNN, a dynamic Graph Neural Network (GNN) framework that captures the dynamic interplay between the EEG electrode locations and the semantics of their corresponding brain regions.
Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting
Forecasting the number of visits to Points-of-Interest (POI) in an urban area is critical for planning and decision-making for various application domains, from urban planning and transportation management to public health and social studies.
Clustering Human Mobility with Multiple Spaces
Human mobility clustering is an important problem for understanding human mobility behaviors (e. g., work and school commutes).
Integer-arithmetic-only Certified Robustness for Quantized Neural Networks
Adversarial data examples have drawn significant attention from the machine learning and security communities.
SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling
Federated learning enables multiple clients, such as mobile phones and organizations, to collaboratively learn a shared model for prediction while protecting local data privacy.
