Grid LSTM Neural Networks for N-Dimensional Data Processing
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Solution Overview
Problem
Current neural network architectures face challenges in effectively modulating N-way communication between layers, leading to inefficient handling of higher-dimensional data such as images, where memory vector growth outpaces computational capacity, affecting performance on machine learning tasks.
Innovation Solution
A grid Long Short-Term Memory (LSTM) neural network architecture is implemented, where multiple N-LSTM blocks are arranged in an N-dimensional grid, allowing for efficient modulation of communication between blocks by applying transforms to input hidden and memory vectors across dimensions, preventing excessive memory vector growth and enabling effective interaction of hidden and memory vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If memory vectors are passed between N-LSTM blocks in a grid architecture, then the network can perform deep and sequential computation, but the values in memory vectors grow combinatorially with the size of each dimension and the total number of dimensions N
Solution Approach 1:
The patent segments the N-way communication between N-LSTM blocks by introducing gating mechanisms that divide and control information flow along different dimensions. Each gate independently regulates the passage of memory vectors, preventing uncontrolled combinatorial growth while maintaining the segmented computational pathways needed for deep and sequential computation.
Solution Approach 2:
The patent applies parameter changes by using learned gate parameters to dynamically modulate the strength and direction of communication between blocks. These parameter-based gates transform the raw combinatorial communication into controlled, regulated information flow, allowing the system to adapt memory vector propagation to the specific computational needs of different dimensions.
2Adaptability or versatility
If multiple N-LSTM blocks are arranged in an N-dimensional grid to handle higher-dimensional data, then the network can process complex data structures, but the complexity of the architecture increases
Solution Approach 1:
The patent implements universality by designing the N-LSTM block as a multi-functional unit that can handle multiple dimensions of data simultaneously through its grid arrangement. Each block serves multiple purposes: processing sequential data along time dimensions, spatial dimensions, and interacting with other dimensions through gated connections, thereby reducing overall architectural complexity through functional consolidation.
Solution Approach 2:
The patent resolves complexity by introducing an additional gating dimension that regulates communication between N-LSTM blocks. This gating dimension acts as a control layer that simplifies the management of N-way communication, transforming the complex N-dimensional interaction problem into a more manageable structure where gates independently control each dimensional pathway.
3Productivity
If hidden and memory vectors from different dimensions are allowed to interact, then the network can perform comprehensive computation, but the vectors may become conflated leading to loss of information
Solution Approach 1:
The patent introduces gating mechanisms as intermediary elements between hidden and memory vectors from different dimensions. These gates act as mediators that selectively permit or block interactions, ensuring that comprehensive computation occurs only through controlled pathways. The intermediary gates prevent direct conflation of vectors while still enabling necessary computational interactions.
Solution Approach 2:
The patent implements feedback mechanisms where the state of gates is dynamically adjusted based on the interaction between vectors from different dimensions. This feedback control ensures that when vectors interact to perform comprehensive computation, the system monitors and regulates the interaction strength, preventing excessive conflation that would lead to information loss while maintaining productive computation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing grid Long Short-Term Memory (LSTM) neural networks that includes a plurality of N-LSTM blocks arranged in an N-dimensional grid. Each N-LSTM block is configured to: receive N input hidden vectors, the N input hidden vectors each corresponding to a respective one of the N dimensions; receive N input memory vectors, the N input memory vectors each corresponding to a respective one of the N dimensions; and, for each of the dimensions, apply a respective transform for the dimension to the memory hidden vector corresponding to the dimension and the input hidden vector corresponding to the dimension to generate a new hidden vector corresponding to the dimension and a new memory vector corresponding to the dimension.