3 रिपॉजिटरी
Schema definitions for storing relationship metadata between graphical entities.
Distinguishing note: Focuses on the data structure of relationships rather than the logic of binding.
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This project is a programmable, high-performance drawing engine designed for building collaborative whiteboards, diagramming tools, and infinite canvas applications. It provides a reactive graphics runtime that manages complex canvas interactions, viewport animations, and input handling through a unified signal-based API. The framework is built on a schema-driven data store that maintains application state in a strictly typed, centralized record system, enabling efficient UI updates and persistent data management. The engine distinguishes itself through a highly modular architecture that supp
Defines data structures for identifying and storing relationships between graphical elements.
MJExtension is a JSON serialization library and model mapping framework used to convert data between JSON strings and structured model objects. It functions as an object data mapper that handles the encoding and decoding of complex object hierarchies for network transmission and storage. The framework is a non-intrusive data mapper that uses reflection and runtime inspection to map raw data strings to application objects. This approach allows for data transformation without requiring base class inheritance, decorators, or extensions to the underlying model classes. The system supports recurs
Decouples data transformation logic from model definitions to support plain objects and third-party classes.
This repository is an educational collection of implementations and research notes focused on deep learning architectures and optimization techniques. It provides modular code examples designed to demonstrate foundational and advanced concepts in machine learning, ranging from basic neural network structures to complex training strategies. The project distinguishes itself by offering practical implementations of specialized research methods, including capsule-based feature aggregation, gradient direction decoupling, and self-normalizing weight regularization. These materials allow for the stu
Automates parameter scaling for physical tight-binding models to ensure design constraint satisfaction.