ABrain-One/nn-dataset
Jan 2026 – Jan 2026
1 Month active
Languages Used
JSONPython
Technical Skills
PythonPython programmingSQLalgorithm designdata analysisdatabase management
Anujaya Vijayakumar
PROFILE
Anujaya Vijayakumar
Anuj Ayasig developed a MinHash-based Neural Network Similarity Platform for the ABrain-One/nn-dataset repository, focusing on compact per-model representations and architecture-level similarity indexing to streamline benchmarking and model selection. Leveraging Python and SQL, Anuj implemented per-model neural network statistics using code MinHash and introduced a scalable architecture-level MinHash index, enabling efficient similarity analysis and diversity-aware querying. The platform supports SQL-based retrieval and filtering, allowing for rapid discovery and evaluation of neural network models. This work demonstrated depth in algorithm design and data analysis, resulting in a robust, extensible workflow for model benchmarking and diversity-aware model discovery within the repository.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
1
Lines of code
2,126,665
Activity Months1
Your Network
43 people
Work History
January 2026
8 Commits • 1 Features
Jan 1, 2026January 2026: Delivered a MinHash-based Neural Network Similarity Platform for ABrain-One/nn-dataset, enabling compact per-model representations, architecture-level similarity indexing, and SQL-based retrieval to streamline benchmarking and model selection. Implemented per-model NN statistics with code MinHash, introduced architecture-level MinHash index (nn_sim.jsonl.gz), and enhanced similarity tooling with optional diversity queries and a CLI interface. This work improves model discovery, benchmarking efficiency, and supports scalable, diversity-aware model evaluation.
8 Commits • 1 Features
Jan 1, 2026January 2026: Delivered a MinHash-based Neural Network Similarity Platform for ABrain-One/nn-dataset, enabling compact per-model representations, architecture-level similarity indexing, and SQL-based retrieval to streamline benchmarking and model selection. Implemented per-model NN statistics with code MinHash, introduced architecture-level MinHash index (nn_sim.jsonl.gz), and enhanced similarity tooling with optional diversity queries and a CLI interface. This work improves model discovery, benchmarking efficiency, and supports scalable, diversity-aware model evaluation.
January 2026
Activity
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Quality Metrics
Correctness82.6%
Maintainability82.6%
Architecture87.6%
Performance82.6%
AI Usage42.6%
Skills & Technologies
Programming Languages
JSONPython
Technical Skills
PythonPython programmingSQLalgorithm designdata analysisdatabase managementmachine learningneural networks
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