ABrain-One/nn-dataset
Sep 2025 – Apr 2026
4 Months active
Languages Used
GitPythonJSON
Technical Skills
Deep LearningMachine Learning ExperimentationNeural Network Architecture DesignPyTorchVersion ControlComputer Vision
yLukhi
PROFILE
Ylukhi
Worked on the ABrain-One/nn-dataset repository to design and implement a suite of Mixture-of-Experts (MoE) neural network architectures for image classification, focusing on CIFAR-10 experiments. Developed and integrated MoE models using Python and PyTorch, introducing gating mechanisms, heterogeneous expert ensembles, and mixup data augmentation to improve performance and adaptability. Standardized file and statistics organization, optimized directory structures, and enhanced repository hygiene to support reproducible experimentation and maintainability. Expanded the MoE framework with new models and refined expert selection logic, enabling scalable workflows and efficient onboarding. Maintained robust version control practices throughout, with no reported defects across multiple feature deliveries.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
16Total
Bugs
0
Commits
16
Features
6
Lines of code
18,233,242
Activity Months4
Your Network
58 peopleShared Repositories
58
pritamMember
CSXizhangMember
ahsan89-ossMember
ABrain-OneMember
ABrain-OneMember
ABrain-OneMember
ABrain-OneMember
ABrain-OneMember
ABrain-OneMember
Work History
April 2026
5 Commits • 2 Features
Apr 1, 2026April 2026 (2026-04) Monthly summary for ABrain-One/nn-dataset focused on business value, maintainability, and technical advancement. Key outcomes include: (1) standardized MoE4Own file and statistics organization to enable scalable workflow and reduce risk in production deployments; (2) expansion of the MoE framework with a batch of new models and refined gating to improve expert selection; (3) groundwork for future experiments and onboarding through directory hygiene and path consistency; and (4) no blocking defects reported, with measurable improvements in repository structure and maintainability.
5 Commits • 2 Features
Apr 1, 2026April 2026 (2026-04) Monthly summary for ABrain-One/nn-dataset focused on business value, maintainability, and technical advancement. Key outcomes include: (1) standardized MoE4Own file and statistics organization to enable scalable workflow and reduce risk in production deployments; (2) expansion of the MoE framework with a batch of new models and refined gating to improve expert selection; (3) groundwork for future experiments and onboarding through directory hygiene and path consistency; and (4) no blocking defects reported, with measurable improvements in repository structure and maintainability.
April 2026
March 2026
5 Commits • 1 Features
Mar 1, 2026March 2026 (2026-03) monthly work summary for ABRAIN-One/nn-dataset. Focused on delivering an MoE (Mixture of Experts) Model Suite to boost performance, adaptability, and evaluation capabilities. Implemented MoE4 model generation with gating mechanisms, training statistics collection, and mixup data augmentation. Delivered a batch of successful MoE variants across multiple architectures and established an end-to-end model generation/evaluation workflow.
March 2026
5 Commits • 1 Features
Mar 1, 2026March 2026 (2026-03) monthly work summary for ABRAIN-One/nn-dataset. Focused on delivering an MoE (Mixture of Experts) Model Suite to boost performance, adaptability, and evaluation capabilities. Implemented MoE4 model generation with gating mechanisms, training statistics collection, and mixup data augmentation. Delivered a batch of successful MoE variants across multiple architectures and established an end-to-end model generation/evaluation workflow.
October 2025
4 Commits • 1 Features
Oct 1, 2025October 2025 — NN-dataset (ABrain-One/nn-dataset): Focused MoE feature delivery and experimentation with CIFAR-10. Delivered the Mixture-of-Experts (MoE) Image Classification Experiment Suite, introducing AlexNet-based experts and gating networks to evaluate performance and efficiency. Extended MoE to heterogeneous ensembles across AlexNet, AirNet, BagNet, and DenseNet, and integrated MoE with existing simpler architectures. Documented CIFAR-10 results and progress to enable rapid iteration and traceability. No critical bugs reported; core MoE pipelines remained stable, enabling continued experimentation and future deployments. Technologies demonstrated include MoE architectures, gating networks, ensemble methods, CIFAR-10 experiments, and robust experiment tracking.
4 Commits • 1 Features
Oct 1, 2025October 2025 — NN-dataset (ABrain-One/nn-dataset): Focused MoE feature delivery and experimentation with CIFAR-10. Delivered the Mixture-of-Experts (MoE) Image Classification Experiment Suite, introducing AlexNet-based experts and gating networks to evaluate performance and efficiency. Extended MoE to heterogeneous ensembles across AlexNet, AirNet, BagNet, and DenseNet, and integrated MoE with existing simpler architectures. Documented CIFAR-10 results and progress to enable rapid iteration and traceability. No critical bugs reported; core MoE pipelines remained stable, enabling continued experimentation and future deployments. Technologies demonstrated include MoE architectures, gating networks, ensemble methods, CIFAR-10 experiments, and robust experiment tracking.
October 2025
September 2025
2 Commits • 2 Features
Sep 1, 2025Monthly summary for 2025-09 for repository ABrain-One/nn-dataset focusing on key accomplishments, business value, and technical achievements.
September 2025
2 Commits • 2 Features
Sep 1, 2025Monthly summary for 2025-09 for repository ABrain-One/nn-dataset focusing on key accomplishments, business value, and technical achievements.
Activity
Loading activity data...
Quality Metrics
Correctness89.4%
Maintainability86.2%
Architecture94.4%
Performance86.2%
AI Usage35.0%
Skills & Technologies
Programming Languages
GitJSONPython
Technical Skills
Computer VisionData AnalysisData ManagementDeep LearningFile OrganizationMachine LearningMachine Learning ExperimentationModel ArchitectureModel IntegrationModel OptimizationNeural Network Architecture DesignNeural NetworksPyTorchPythonPython Programming
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline