Franklin Moormann

Fix issue 418 and update info

1

This commit implements comprehensive uncertainty quantification capabilities for Phase 3, addressing all requirements specified in Issue #418. ## Bayesian Neural Networks ### Monte Carlo Dropout - MCDropoutLayer: Dropout layer that...

Fix Issue 420 prompt engineering

1

…ssue #420) This commit implements a complete prompt engineering infrastructure including: Core Components: - Prompt Templates: Simple, FewShot, Chat, ChainOfThought, and ReAct templates - Tool/Function Calling: FunctionToolBase, ToolRegistry, and schema...

# AiDotNet Platform Integration - Epic ## Executive Summary Transform AiDotNet from a library-only solution into a complete platform ecosystem that enables web-based model creation, deployment, and monetization through a...

[Phase 2 Parity] Implement Standard CNN Architectures

1

Problem: AiDotNet has ViT and Transformer but lacks foundational CNNs. Missing: ResNet 18/34/50/101/152 (CRITICAL), VGG 11/13/16/19 (CRITICAL), Inception v3/v4 (HIGH), DenseNet 121/169/201/264 (HIGH), MobileNet v2/v3 (HIGH), EfficientNet B0-B7 (HIGH). Existing:...

[Phase 3] Implement Adversarial Robustness and AI Safety

1

## Problem MISSING: AI safety, adversarial robustness, and alignment techniques. ## Existing - Issue #287: Safety filtering - Basic security policy (SECURITY.md) ## Missing Implementations **Adversarial Training (CRITICAL):** - FGSM...

[Phase 3] Implement Prompt Engineering and LLM Tools Framework

1

## Problem MISSING: Prompt engineering infrastructure for working with large language models. ## Missing Implementations **Prompt Templates (CRITICAL):** - Template engine (variable substitution) - Few-shot prompt builders - System/User/Assistant message...

[Phase 3] Implement Continual Learning and Active Learning

1

## Problem MISSING: Continual/lifelong learning (learn without forgetting) and active learning (smart data labeling). ## Missing Implementations **Continual Learning (HIGH):** - Elastic Weight Consolidation (EWC) - Progressive Neural Networks -...

[Phase 3] Implement Uncertainty Quantification and Bayesian Neural Networks

1

## Problem MISSING: Uncertainty quantification is critical for reliable AI, especially in safety-critical applications. ## Missing Implementations **Bayesian Neural Networks (CRITICAL):** - Variational inference (Bayes by Backprop) - Monte Carlo...

[Phase 3] Implement Chain-of-Thought and Advanced Reasoning

1

## Problem COMPLETELY MISSING: Chain-of-thought reasoning is critical for modern LLMs and reasoning tasks. ## Missing Implementations **Chain-of-Thought (CRITICAL):** - CoT prompting framework - Few-shot CoT examples - Zero-shot CoT...

[Phase 3] Implement Generative Adversarial Networks GANs

1

## Problem MISSING: GANs are a major class of generative models, completely absent from AiDotNet. ## Existing - Diffusion models (issues #261-266, #298) - VAE (in progress per worktree branch)...