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Radu State

Identifiers

  • name variant Radu State 0.60 · backfill

Papers (24)

  1. Attraction, Repulsion, and Friction: Introducing DMF, a Friction-Augmented Drifting Model cs.LG · 2026 · author #5
  2. Geometric Entropy and Retrieval Phase Transitions in Continuous Thermal Dense Associative Memory cond-mat.dis-nn · 2026 · author #3
  3. Low-Complexity Algorithm for Stackelberg Prediction Games with Global Optimality eess.SP · 2026 · author #6
  4. How Much Does Persuasion Strategy Matter? LLM-Annotated Evidence from Charitable Donation Dialogues cs.CL · 2026 · author #3
  5. Vision Transformer-Based Time-Series Image Reconstruction for Cloud-Filling Applications cs.CV · 2025 · author #3
  6. PHom-GeM: Persistent Homology for Generative Models cs.LG · 2019 · author #2
  7. Predicting Sparse Clients' Actions with CPOPT-Net in the Banking Environment cs.LG · 2019 · author #2
  8. User-Device Authentication in Mobile Banking using APHEN for Paratuck2 Tensor Decomposition cs.NA · 2019 · author #3
  9. Non-Negative PARATUCK2 Tensor Decomposition Combined to LSTM Network For Smart Contracts Profiling cs.CE · 2019 · author #2
  10. Infer Your Enemies and Know Yourself, Learning in Real-Time Bidding with Partially Observable Opponents cs.GT · 2019 · author #7
  11. Improving Missing Data Imputation with Deep Generative Models cs.LG · 2019 · author #3
  12. The Art of The Scam: Demystifying Honeypots in Ethereum Smart Contracts cs.CR · 2019 · author #3
  13. Generating Multi-Categorical Samples with Generative Adversarial Networks stat.ML · 2018 · author #3
  14. Impact of Biases in Big Data cs.LG · 2018 · author #3
  15. On the Reduction of Biases in Big Data Sets for the Detection of Irregular Power Usage cs.LG · 2018 · author #2
  16. Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations cs.LG · 2017 · author #6
  17. Human in the Loop: Interactive Passive Automata Learning via Evidence-Driven State-Merging Algorithms stat.ML · 2017 · author #2
  18. The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study cs.LG · 2017 · author #8
  19. Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses? cs.LG · 2017 · author #5
  20. Interpreting Finite Automata for Sequential Data stat.ML · 2016 · author #4
  21. Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets cs.LG · 2016 · author #4
  22. The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey cs.AI · 2016 · author #4
  23. Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets cs.LG · 2016 · author #4
  24. Torinj : Automated Exploitation Malware Targeting Tor Users cs.CR · 2012 · author #3

Mentions

  • 1208.2877 #3 · backfill · confidence 0.70 Radu State

Frequent Coauthors