Steering Deep Sequence Models via Prototype Adjustment
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Solution Overview
Problem
Deep sequence models, such as RNNs, face challenges in interpretability and steerability, limiting their adoption in critical decision-making scenarios where understanding predictions and incorporating domain expert insights is necessary.
Innovation Solution
A visual interface is developed to display and interact with prototype sequences learned by the model, allowing users to fine-tune the model by adjusting these sequences, which includes statistical information and updates, enabling domain experts to steer the model without relying on machine learning practitioners.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep sequence models are used to achieve state-of-the-art results in sequence data analysis, then predictive accuracy is improved, but interpretability deteriorates due to complex architecture and massive model weights
Solution Approach 1:
The patent introduces prototypes as intermediary representations that bridge the gap between complex deep sequence models and human interpreters. These prototypes serve as mediating concepts that capture essential patterns from training data while remaining comprehensible to domain experts, thereby maintaining high predictive accuracy while improving interpretability through a layer of abstraction.
Solution Approach 2:
The patent creates simplified copies of the complex model's knowledge in the form of prototype sequences. Instead of directly interpreting massive model weights and complex architectures, the system generates representative prototype examples that copy the essential decision-making patterns of the trained model, making the underlying logic accessible and understandable to human users.
2Ease of manufacture
If end-to-end training is used to alleviate manual data feature curation, then ease of manufacture is improved, but steerability deteriorates as expert users cannot directly steer the model with their insights
Solution Approach 1:
The patent implements a feedback mechanism where domain experts can provide insights and domain knowledge that are incorporated into prototype adjustments. This feedback loop allows expert users to steer the model by modifying prototypes, which then influences the model's predictions. The system maintains the benefits of end-to-end training while adding a layer of expert-controlled steering through prototype manipulation.
Solution Approach 2:
The patent transforms the static, fixed model from end-to-end training into a dynamic system where prototypes can be adjusted based on expert feedback. This dynamic capability allows the model to adapt and be steered by domain experts without requiring manual feature curation, combining the efficiency of automated training with the flexibility of expert guidance.
3Adaptability or versatility
If domain expert insights are incorporated to improve model steerability, then adaptability is improved, but device complexity increases due to additional interaction interfaces and feedback mechanisms
Solution Approach 1:
The patent uses prototypes as an intermediary layer that simplifies the interaction between domain experts and the complex deep sequence model. Instead of requiring experts to directly manipulate complex model weights or architectures, they interact with simplified prototype representations, reducing the perceived system complexity while maintaining steerability.
Data Source
AI summary
A deep sequence model with prototypes may be steered. A prototype overview is displayed, the prototype overview including a plurality of prototype sequences learned by a model through backpropagation, each of the prototype sequences including a series of events, where for each of the prototype sequences, statistical information is presented with respect to use of the prototype sequence by the model. Input is received adjusting one or more of the prototype sequences to fine-tune the model. The model is updated using the plurality of prototype sequences, as adjusted, to create an updated model. The model, as updated, is displayed in the prototype overview.