PPaperPicks

Yongshun Gong

22 papers at tracked venues · 14 at CORE A* · active 20242026

Venues

Frequent coauthors

Papers

  1. Enhancing the Transferability of Jailbreak Attacks on Large Language Models via Exploiting Reparameterization Invariance
  2. Retriever Encoder Selection Matters for In-Context Learning-based Medical Segmentation
  3. An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Models
  4. CAN-ST: Clustering Adaptive Normalization for Spatio-temporal OOD Learning
  5. CodeV: Issue Resolving with Visual Data
  6. Content-aware Balanced Spectrum Encoding in Masked Modeling for Time Series Classification
  7. PSReg: Prior-guided Sparse Mixture of Experts for Point Cloud Registration
  8. SeqMvRL: A Sequential Fusion Framework for Multi-view Representation Learning
  9. Spatio-temporal Prototype-based Hierarchical Learning for OD Demand Prediction
  10. Time-aware Medication Recommendation via Intervention of Dynamic Treatment Regimes
  11. Towards Region-Adaptive Feature Disentanglement and Enhancement for Small Object Detection
  12. 3DBench: A Scalable 3D Benchmark and Instruction-Tuning Dataset
  13. CodeM: Less Data Yields More Versatility via Ability Matrix
  14. Exploring Channel-Aware Typical Features for Out-of-Distribution Detection
  15. Exploring Urban Semantics: A Multimodal Model for POI Semantic Annotation with Street View Images and Place Names
  16. Going Where, by Whom, and at What Time: Next Location Prediction Considering User Preference and Temporal Regularity
  17. Learning Hierarchy-Enhanced POI Category Representations Using Disentangled Mobility Sequences
  18. Point Cloud Pre-Training with Diffusion Models
  19. Profiling Urban Streets: A Semi-Supervised Prediction Model Based on Street View Imagery and Spatial Topology
  20. Spatio-temporal Graph Normalizing Flow for Probabilistic Traffic Prediction
  21. Time-Series Representation Learning via Dual Reference Contrasting
  22. Urban Region Embedding via Multi-View Contrastive Prediction