KDD Cup 2013 Track 1 - Author - Paper Identification challenge (2nd place team)
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We describe our approach for solution of Author - Paper Identification Challenge: https://www.kaggle.com/c/kdd-cup-2013-author-paper-identification-challenge
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Welcome to UiPath Automation Developer Associate Training Series 2025 - Session 1.
In this session, we will cover the following topics:
Introduction to RPA & UiPath Studio
Overview of RPA and its applications
Introduction to UiPath Studio
Variables & Data Types
Control Flows
You are requested to finish the following self-paced training for this session:
Variables, Constants and Arguments in Studio 2 modules - 1h 30m - https://academy.uipath.com/courses/variables-constants-and-arguments-in-studio
Control Flow in Studio 2 modules - 2h 15m - https:/academy.uipath.com/courses/control-flow-in-studio
鏝 For any questions you may have, please use the dedicated Forum thread. You can tag the hosts and mentors directly and they will reply as soon as possible. Blockchain for Businesses Practical Use Cases & Benefits.pdf
Blockchain for Businesses Practical Use Cases & Benefits.pdf Yodaplus Technologies Private Limited
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Blockchain is revolutionizing industries by enhancing security, transparency, and automation. From supply chain management and finance to healthcare and real estate, blockchain eliminates inefficiencies, prevents fraud, and streamlines operations.
What You'll Learn in This Presentation:
1. How blockchain enables real-time tracking & fraud prevention
2. The impact of smart contracts & decentralized finance (DeFi)
3. Why businesses should adopt secure and automated blockchain solutions
4. Real-world blockchain applications across multiple industries
Explore the future of blockchain and its practical benefits for businesses!Revolutionizing Field Service: How LLMs Are Powering Smarter Knowledge Access...
Revolutionizing Field Service: How LLMs Are Powering Smarter Knowledge Access...Earley Information Science
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What is FinTech A Complete Guide to Financial Technology.pdf
What is FinTech A Complete Guide to Financial Technology.pdf Yodaplus Technologies Private Limited
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Blockchain for Businesses Practical Use Cases & Benefits.pdf
Blockchain for Businesses Practical Use Cases & Benefits.pdf Yodaplus Technologies Private Limited
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KDD Cup 2013 Track 1 - Author - Paper Identification challenge (2nd place team)
- 1. KDD Cup 2013 Author Paper Identification Challenge (2nd place team) Dmitry Efimov Lucas Silva Benjamin Solecki
- 2. Approach summary Goal: find incorrectly assigned pairs author-paper Supervised machine learning problem with binary response Deep feature engineering (> 300 features) Gradient Boosting Machine (package gbm in R)
- 3. Author Paper graph
- 4. Author features count journals tf-idf measure Count features NLP features Multiple source features authors duplicates
- 5. Paper features Count features NLP features Multiple source features Additional features count keywords tf-idf measure papers duplicates reverse features engineering
- 6. Author paper features (1 of 4) Count features Multiple source features Additional features Likelihood features
- 7. Author paper features (2 of 4) Count features Additional features count of coauthors with the same affiliation reverse feature engineering: year ranking feature
- 8. Author paper features (3 of 4) Multiple source features how many times pair author-paper appeared in the Microsoft database?
- 9. Author paper features (4 of 4) Likelihood features use Lj and Lja as features 1) use (留 Lj + (1留) Lja) as feature (shrunken likelihood); 2) mixed-effects models (package lme4 in R) to find 留 Lj likelihood by journal Lja likelihood by journal and author
- 10. Model Gradient Boosting Machine (package gbm in R) Grid search for the set of parameters 83 features in the final model (out of 300 calculated features )
- 11. Result and conclusion Our MAP score is 0.98144 (the winning submission score is 0.98259). Many algorithms (LambdaRank, LambdaMART, RankBoost) based on MAP optimization gave less MAP score than GBM with Bernoulli distribution. The idea of feature classification based on bipartite author-paper graph is very promising. Analyzing of graph topology can give ideas for new features.
- 12. Thank you!