MonashDeepNeuron/Neural-Cellular-Automata
Nov 2024 – Nov 2024
1 Month active
Languages Used
JinjaJupyter NotebookPython
Technical Skills
Data AnalysisDeep LearningHyperparameter TuningMachine LearningModel TrainingPyTorch
chloekoee
PROFILE
Chloekoee
Chloe Koe developed a Dynamic Learning Rate Adjustment System for the MonashDeepNeuron/Neural-Cellular-Automata repository, focusing on improving neural network training efficiency and reproducibility. She implemented a modular learning_rate_adjuster in Python and PyTorch, enabling training scripts to adaptively modify learning rates based on historical loss values. Her approach included loss aggregation and outlier filtering, with groundwork laid for a turbulence bias mechanism to guide future enhancements. By introducing train_lra.py and updating core training workflows, Chloe provided a repeatable method for hyperparameter tuning. This work demonstrated depth in data analysis and model training, addressing practical challenges in deep learning experimentation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
744
Activity Months1
Your Network
8 people
Work History
November 2024
2 Commits • 1 Features
Nov 1, 2024Month 2024-11: Delivered Dynamic Learning Rate Adjustment System for Neural-Cellular-Automata. Implemented train_lra.py and a learning_rate_adjuster module to modify the training learning rate based on historical loss values. Updated training scripts to use the adjuster. Core logic includes loss aggregation and outlier filtering; turbulence bias introduced but not fully implemented. This work provides a repeatable mechanism for LR tuning across experiments, enabling more efficient training and reproducibility. All changes are contained within MonashDeepNeuron/Neural-Cellular-Automata.
2 Commits • 1 Features
Nov 1, 2024Month 2024-11: Delivered Dynamic Learning Rate Adjustment System for Neural-Cellular-Automata. Implemented train_lra.py and a learning_rate_adjuster module to modify the training learning rate based on historical loss values. Updated training scripts to use the adjuster. Core logic includes loss aggregation and outlier filtering; turbulence bias introduced but not fully implemented. This work provides a repeatable mechanism for LR tuning across experiments, enabling more efficient training and reproducibility. All changes are contained within MonashDeepNeuron/Neural-Cellular-Automata.
November 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JinjaJupyter NotebookPython
Technical Skills
Data AnalysisDeep LearningHyperparameter TuningMachine LearningModel TrainingPyTorchPython Scripting
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