External Memory Temporal Models for Long-Range Sequence Prediction
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Solution Overview
Problem
Conventional neural network systems that use internal states for both memory storage and computation face challenges in efficiently storing and retrieving information over long time steps, leading to increased parametric complexity and degraded performance in generating accurate sequences of predicted observations.
Innovation Solution
The system employs an external memory that separates memory storage from computation, using a controller recurrent neural network and a decoder neural network to process inputs and generate outputs, while an attention neural network and prior/posterior generation networks manage latent variables and context vectors to adaptively update memory, capturing long-range dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional neural networks use internal states for both memory storage and computation, then the system structure is simple, but the performance degrades over long time steps and parametric complexity increases
Solution Approach 1:
The patent divides the neural network into separate functional modules: a controller RNN for computation, an attention network for memory access, and a decoder for observation generation. This segmentation allows each component to specialize in its function, improving overall performance while managing complexity through modular design.
Solution Approach 2:
The patent extracts the memory storage function from the computational RNN and places it in external memory. The RNN controller only performs computation and generates attention weights, while the attention network handles memory access. This extraction resolves the contradiction by separating memory and computation, improving reliability without significantly increasing complexity.
2Duration of action of moving object
If external memory is introduced to separate storage from computation, then performance improves over long time steps, but device complexity increases
Solution Approach 1:
The attention network serves as an intermediary between the RNN controller and external memory. It translates the controller's hidden state into attention weights that selectively access relevant memory locations. This intermediary enables efficient long-term memory access without requiring the controller to directly manage memory, balancing extended time step coverage with manageable complexity.
Solution Approach 2:
The external memory structure provides universal storage capabilities that can be accessed at any time step through attention mechanisms. The same memory structure handles both short-term and long-term dependencies uniformly, allowing the system to maintain performance over extended time steps while using a consistent architectural pattern.
3Measurement precision
If memory capacity is increased to capture long-range dependencies, then prediction accuracy improves, but computational resources increase
Solution Approach 1:
Instead of uniformly accessing all memory locations, the attention mechanism performs partial action by selectively focusing on relevant memory locations based on the current task and time step. This selective access maintains high prediction accuracy for long-range dependencies while significantly reducing computational resources compared to processing the entire memory space.
Solution Approach 2:
The attention mechanism dynamically changes parameters (attention weights) based on the input sequence and task requirements. This allows the system to adaptively allocate computational resources to the most relevant memory locations, maintaining high prediction accuracy while optimizing resource usage by ignoring irrelevant memory regions.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating sequences of predicted observations, for example images. In one aspect, a system comprises a controller recurrent neural network, and a decoder neural network to process a set of latent variables to generate an observation. An external memory and a memory interface subsystem is configured to, for each of a plurality of time steps, receive an updated hidden state from the controller, generate a memory context vector by reading data from the external memory using the updated hidden state, determine a set of latent variables from the memory context vector, generate a predicted observation by providing the set of latent variables to the decoder neural network, write data to the external memory using the latent variables, the updated hidden state, or both, and generate a controller input for a subsequent time step from the latent variables.