Visual ML Model Creation With Dynamic Training Variables
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Solution Overview
Problem
Conventional programming libraries for neural networks are complex, resource-intensive, and limit creative configuration, while traditional programming languages require extensive manual coding and introduce biases during training.
Innovation Solution
A user interface for visually creating machine learning models with mathematical expressions, allowing dynamic variable assignment during training, and a compiler that handles unassigned variables without errors, reducing overhead and improving model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional programming libraries are used for neural networks, then neural network functionality can be implemented, but the software becomes complex and resource-intensive requiring sophisticated hardware
Solution Approach 1:
The patent extracts only the essential neural network functionality from complex programming libraries, implementing a minimalistic framework that provides core neural network capabilities without the unnecessary complexity of existing libraries. This allows neural network functionality to be maintained while significantly reducing software complexity and resource requirements.
Solution Approach 2:
The patent employs a lightweight, simple data structure design that uses minimal memory resources, effectively replacing the heavy, complex data structures of conventional libraries. This approach enables neural networks to run on less sophisticated hardware while maintaining functionality.
2Ease of manufacture
If conventional programming libraries are used, then neural network design is facilitated, but the pool of proficient developers is limited due to the complexity of software stacks
Solution Approach 1:
The patent uses a simplified programming interface with basic data structures that are easier to understand and work with, removing the need for developers to master complex software stacks. This democratizes neural network development by making it accessible to a broader range of developers without requiring extensive expertise in sophisticated libraries.
3Adaptability or versatility
If traditional programming languages are used for neural networks, then flexibility in coding is achieved, but extensive manual coding is required and biases are introduced during training
Solution Approach 1:
The patent introduces a domain-specific language that allows neural network definitions to be written in a simplified syntax that compiles to efficient machine code. This eliminates the need for extensive manual coding while maintaining flexibility, as developers can express neural network architectures concisely without the overhead of traditional programming language verbosity.
4Reliability
If conventional programming libraries are used, then neural network training can be performed, but processing time and resource consumption are increased
Solution Approach 1:
The patent extracts and implements only the essential training functionality needed for neural networks, eliminating the overhead of complex library infrastructure. This results in faster training times and lower resource consumption while maintaining the ability to perform reliable model training.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for creating a machine learning model. The technique includes generating a user interface comprising one or more components for visually generating the machine learning model. The technique also includes modifying source code specifying a plurality of mathematical expressions that define the machine learning model based on user input received through the user interface. The technique further includes compiling the source code into compiled code that, when executed, causes one or more parameters of the machine learning model to be learned during training of the machine learning model.