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
Developed a dynamic learning rate adjustment system for the MonashDeepNeuron/Neural-Cellular-Automata repository, enabling adaptive modification of training learning rates based on historical loss trends. The solution introduced a modular learning_rate_adjuster and a dedicated training script, train_lra.py, both implemented in Python and leveraging PyTorch for model training. The core logic aggregated loss values and filtered outliers to inform learning rate updates, with groundwork laid for future enhancements such as turbulence bias. This approach supports more efficient and reproducible deep learning experiments by automating hyperparameter tuning, and demonstrates proficiency in data analysis, machine learning, and Python scripting within research codebases.
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 peopleShared Repositories
8
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
Repositories Contributed To
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