Input-Output Example Encoding for Neural Program Induction
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Solution Overview
Problem
Existing neural network architectures for program induction are computationally expensive, hard to train, and require separate models for each task, making them inefficient and difficult to interpret.
Innovation Solution
The use of a cross-correlation input/output network and a recursive-reverse-recursive neural network (R3NN) to encode input-output examples and generate programs incrementally, allowing for the construction of interpretable programs consistent with given specifications without explicit search over the vast space of possible programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional neural network architectures are used for program induction, then the model can learn mappings from input-output examples, but the computational cost becomes expensive and training becomes difficult
Solution Approach 1:
The patent segments the program induction task into character-level processing using LSTM networks. Instead of processing entire programs at once, the system processes input and output examples character by character, generating feature vectors that capture local patterns. This segmentation reduces computational complexity while maintaining induction accuracy.
Solution Approach 2:
The patent introduces cross-correlation as an intermediary mechanism between input and output feature vectors. The cross-correlation operation efficiently identifies matching patterns without requiring exhaustive search through the program space, reducing computational cost while preserving the ability to induce correct programs.
2Adaptability or versatility
If traditional neural network architectures are used for program induction, then the model can generalize to new test inputs, but the model requires separate training for each task and is hard to train
Solution Approach 1:
The patent creates a universal program induction system that can handle multiple tasks through a single trained model. The LSTM-based architecture with cross-correlation learns task-agnostic features from input-output examples, enabling the same model to generalize across different program induction tasks without requiring separate training for each task.
Solution Approach 2:
The patent transforms the program induction problem into a parameter estimation problem where the system learns optimal parameters for character processing and cross-correlation operations. By changing the representation parameters to character-level features and using cross-correlation instead of exhaustive search, the system becomes easier to train while maintaining generalization capability.
3Reliability
If exhaustive search over program space is used, then complete programs can be found consistent with specifications, but the search space is vast and the process is inefficient
Solution Approach 1:
The patent performs preliminary character-level processing of input-output examples to generate feature vectors before attempting program synthesis. By pre-processing the examples at the character level and computing cross-correlations in advance, the system narrows down the relevant program patterns before full synthesis, making the process more efficient while ensuring correctness.
Solution Approach 2:
The patent replaces the mechanical exhaustive search process with a neural network-based cross-correlation mechanism. Instead of systematically exploring the entire program space, the LSTM networks with cross-correlation operations efficiently identify relevant program patterns through learned feature representations, dramatically improving productivity while maintaining reliability.
4Adaptability or versatility
If complex neural network models are used for program induction, then the model can capture complex mappings, but the models become difficult to interpret or verify
Solution Approach 1:
The patent creates interpretable copies of the program induction process by generating feature vectors that represent character-level patterns. These feature vectors serve as intermediate representations that can be inspected and verified, providing interpretability while the underlying LSTM networks capture complex mappings. The cross-correlation operations also provide transparent pattern matching that can be analyzed.
Data Source
AI summary
Generally discussed herein are devices, systems, and methods for encoding input-output examples. A method of generating a program using an encoding of input-output examples, may include processing an input example of the input-output examples, using a first long short term memory (LSTM) neural network, one character at a time to produce an input feature vector, processing an output example associated with the input example in the input-output examples, using the LSTM neural network, one character at a time to produce an output feature vector, determining (a) a cross-correlation between the input feature vector and the output feature vector or (b) previously computed feature vectors for a different input-output example that are sufficiently close to the input feature vector and the output feature vector, respectively, and using the determined cross-correlation or previously computed vector, generating a program consistent with the input example and the output example.