RNN Hidden State Segmentation for Asynchronous Sequence Learning
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Solution Overview
Problem
Recurrent neural networks (RNNs) face challenges in handling asynchronous and heterogeneous data sequences, as they struggle to treat data items of different types differently and maintain an accurate hidden state representative of all input data items, limiting their ability to learn interactions between sequences.
Innovation Solution
The method involves splitting the hidden state into portions based on data item types and using an attention mechanism to update these portions, incorporating indicators such as Boolean flags or flag tensors to manage the hidden state segments effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a recurrent neural network maintains a single hidden state representative of all input data items, then the model structure remains simple, but the ability to treat data items of different types differently is restricted and the hidden state becomes inadequate
Solution Approach 1:
The patent divides the single hidden state into multiple separate hidden states, each corresponding to a specific data type. This segmentation allows each hidden state to specialize in representing its associated data type, improving the accuracy and appropriateness of state representation without requiring a single overly complex unified state.
Solution Approach 2:
The patent implements different processing mechanisms for different portions of the hidden state corresponding to different data types. Each data type can have its own update rules, attention mechanisms, or processing parameters, allowing the model to apply locally optimized processing to each data type while maintaining overall system coherence.
2Device complexity
If a recurrent neural network processes each data item serially and updates the hidden state based on each item, then the processing approach remains simple, but the ability to learn interactions between different sequences of data is limited
Solution Approach 1:
The patent introduces an attention mechanism as an intermediary component that operates between the serial processing of data items and the hidden state updates. This attention mechanism enables the model to selectively focus on relevant data items from different sequences and capture interactions between them, while the overall serial processing framework remains intact.
3Ease of manufacture
If a recurrent neural network uses a single hidden state for all data types, then the implementation remains straightforward, but the model becomes unable to learn interactions between different sequences of data
Solution Approach 1:
The patent segments the unified hidden state into multiple type-specific hidden states, enabling the model to maintain separate representations for different data types. This segmentation is implemented in a modular fashion that preserves implementation simplicity while enabling the model to learn interactions between sequences through dedicated processing paths for each data type.
Data Source
AI summary
A method for recurrent neural networks for asynchronous sequences may include receiving first input data associated with a plurality of first data items ordered in a first sequence and second input data associated with a plurality of second data items ordered in a second sequence. Each first data item may be of a first type, and each second data item may be of a second type. Each respective data item of the first and second data items may be inputted with an indicator associated with a respective type of the respective data item to a recurrent unit of a recurrent neural network (RNN). A respective portion of a hidden state may be determined based on the indicator. The respective portion of the hidden state may be updated based on the respective data item and the indicator. A system and computer program product are also disclosed.