Franklin Moormann

[Phase 3] Expand Graph Neural Networks Beyond GCN

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## Problem Has GraphConvolutionalLayer but missing major GNN architectures. ## Existing - src/NeuralNetworks/Layers/GraphConvolutionalLayer.cs - src/RetrievalAugmentedGeneration/KnowledgeGraph.cs (graph storage) ## Missing Implementations **Core GNN Architectures (CRITICAL):** - GAT (Graph Attention Networks) -...

[Phase 3] Implement Physics-Informed Neural Networks and Neural Operators

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## Problem Has symbolic regression but missing PINNs and neural operators for scientific computing. ## Existing - src/Regression/SymbolicRegression.cs ## Missing Implementations **Physics-Informed NNs (CRITICAL):** - PINN (Physics-Informed Neural Network) -...

[Phase 3] Implement 3D AI Point Clouds and Neural Radiance Fields

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## Problem COMPLETELY MISSING: 3D understanding, point cloud processing, and novel view synthesis. ## Missing Implementations **Point Cloud Processing (CRITICAL):** - PointNet (direct point cloud processing) - PointNet++ (hierarchical feature...

[Phase 3] Implement Federated Learning Framework

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## Problem COMPLETELY MISSING: Privacy-preserving distributed machine learning. ## Missing Implementations **Core Algorithms (CRITICAL):** - FedAvg (Federated Averaging) - FedProx (handles system heterogeneity) - FedBN (Batch Normalization handling) **Privacy (HIGH):**...

[Phase 3] Expand Meta-Learning Beyond MAML Reptile SEAL

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## Problem Only 3 meta-learning algorithms exist: MAML, Reptile, SEAL. Missing major metric-based and optimization-based methods. ## Existing - MAML, iMAML, ALFA (gradient-based) - Reptile (gradient-based) - SEAL (self-ensembling) ##...

[Phase 3] Implement Audio AI and Music Information Retrieval

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## Problem COMPLETELY MISSING: Audio understanding, music analysis, and audio fingerprinting capabilities. ## Missing Implementations **Audio Fingerprinting (CRITICAL):** - Shazam-like audio identification - Chromaprint algorithm - Spectrogram-based fingerprinting **Music Information...

[Phase 3] Implement Self-Supervised Learning Methods

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## Problem COMPLETELY MISSING: Self-supervised learning is critical for learning from unlabeled data. ## Missing Implementations **Contrastive Methods (CRITICAL):** - SimCLR (Simple Framework for Contrastive Learning) - MoCo (Momentum Contrast)...

[Phase 2 Parity] Implement Comprehensive Classification Metrics

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Problem: Issue 333 mentions validation but lacks metric implementations. Missing: Accuracy (CRITICAL), Precision/Recall/F1 (CRITICAL), Confusion Matrix (CRITICAL), ROC-AUC (CRITICAL), PR-AUC (CRITICAL), Matthews Correlation (HIGH), Cohen Kappa (HIGH), Hamming Loss (HIGH),...

[Phase 2 Parity] Implement Advanced Augmentation Mixup and CutMix

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Problem: Missing modern augmentation for state-of-the-art training. Missing: Mixup (HIGH), CutMix (HIGH), AutoAugment (MEDIUM), RandAugment (MEDIUM), TrivialAugment (MEDIUM). Use Cases: Improved generalization, calibrated predictions, competition techniques. Architecture: src/Images/Augmentation/Advanced/. Goal: Batch-level...

[Phase 2 Parity] Implement Standard Image Data Augmentation

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Problem: Issue 330 covers preprocessing but not training augmentation. Missing: RandomCrop (CRITICAL), RandomHorizontalFlip (CRITICAL), ColorJitter (HIGH), RandomRotation (HIGH), RandomResizedCrop (HIGH), Normalize (HIGH), GaussianBlur (MEDIUM), RandomAffine (MEDIUM). Architecture: src/Images/Augmentation/. Note: Complements...