Unsupervised Sequence Learning via Reinforcement Learning Encoder-Decoder
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current unsupervised sequence learning methods face challenges in transforming long input sequences of real-valued vectors into shorter symbolic sequences without labeled training data, as they struggle to identify meaningful subsequences and symbols while maintaining data representation accuracy.
Innovation Solution
A method utilizing reinforcement learning and neural networks, specifically an encoder-decoder system where the encoder transforms input sequences into symbolic representations and the decoder reconstructs the original sequences, updating the model to maximize the expected end reward based on the comparison between input and decoded sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If unsupervised learning methods are used to transform long input sequences into shorter symbolic sequences, then human intervention and labeled data requirements are reduced, but the ability to identify meaningful subsequences and symbols while maintaining data representation accuracy deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the decoder's reconstruction of sequences provides information back to the encoder, allowing the system to iteratively improve its symbolic representations. The reinforcement learning component uses the comparison between original and decoded sequences to adjust encoding strategies, maintaining accuracy while operating unsupervised.
Solution Approach 2:
The patent introduces symbolic representations as an intermediary layer between the raw input sequences and the decoded output. This intermediary symbolic sequence acts as a compressed representation that captures essential information while enabling the system to operate without labeled data, bridging the gap between automation and accuracy.
2Measurement precision
If manual creation of training pairs is performed, then data representation accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The system performs self-service by automatically generating its own training signals through the reconstruction process. The decoder attempts to reconstruct the input sequence from symbolic representations, and the difference between original and reconstructed sequences serves as the training signal, eliminating the need for manually created labeled training pairs.
Solution Approach 2:
The patent uses copying by creating reconstructed versions of the input sequences through the decoder. These reconstructed sequences serve as proxies for the original data, allowing the system to learn from unlabeled data by comparing and minimizing differences between originals and copies, thereby maintaining training efficiency.
3Extent of automation
If sequence-to-sequence mapping is performed without labeled data, then human cost is reduced, but the ability to capture ordered information and context deteriorates
Solution Approach 1:
The patent employs dynamic recurrent neural networks that can adapt their internal states to capture temporal dependencies and context information in sequences. The RNNs maintain hidden states that evolve over time, allowing the system to dynamically adjust to varying sequence patterns and retain contextual information without requiring labeled data.
Data Source
AI summary
A system and method provide a sequence learning model. The method for training the sequence learning model comprises retrieving input sequence data. The input sequence data includes one or more input time sequences. The method also encodes the input sequence data into output symbol data using a sequence learning model. The output symbol data includes one or more symbolic representations. The method decodes, based on a neural network, the output symbol data to decoded sequence data, where the decoded sequence data includes one or more decoded time sequences that are to match the one or more input time sequences in the input sequence data. The method further compares the decoded sequence data with the input sequence data and updates the sequence learning model based on the comparison.