Tree-Based Sequence Modeling for Deep History and Lower Compute
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional machine learning methods for sequence modeling tasks, such as neural networks, require large numbers of parameters and complex architectures, leading to high computational costs and limited interpretability, and are often limited to processing fixed-length input sequences.
Innovation Solution
A tree-based machine learning model that explicitly represents sequences of data elements as paths in a tree structure, allowing for parallel training and inference, reduced memory usage, and improved interpretability by directly associating parameters with example sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional neural networks are used for sequence modeling, then they can process complex patterns, but they require large numbers of parameters and complex architectures leading to high computational costs
Solution Approach 1:
The patent segments the sequence modeling task into discrete symbolic transitions represented by a finite state machine. Instead of using a monolithic neural network to process entire sequences, the model breaks down sequence processing into individual transitions between symbolic states, where each transition is modeled separately using simple parameterized rules rather than complex neural network layers.
Solution Approach 2:
The patent replaces expensive, complex neural network architectures with simple, lightweight symbolic transition rules. These simple rules act as 'cheap' computational objects that can be easily stored and applied, eliminating the need for large numbers of parameters and complex model structures while maintaining sequence modeling capabilities.
2Reliability
If conventional neural networks are used for sequence modeling, then they can learn from training data, but they have limited interpretability
Solution Approach 1:
The patent creates an interpretable copy of the sequence processing logic using symbolic representations. Instead of relying on the internal weights and activations of a neural network which are difficult to interpret, the model creates explicit symbolic transitions that mirror the sequence processing behavior, making the decision-making process transparent and analyzable.
3Adaptability or versatility
If conventional neural networks are used for sequence modeling, then they can process training sequences, but they are often limited to processing fixed-length input sequences
Solution Approach 1:
The patent implements a dynamic sequence processing approach where the model can handle sequences of arbitrary length through recursive application of symbolic transition rules. The finite state machine structure allows the model to process sequences dynamically, adapting to any length without requiring fixed-size input layers or padding mechanisms typical of conventional neural networks.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training and utilizing a tree-based machine learning model to perform sequence modeling tasks. In one aspect, a method comprises: obtaining a collection of training examples, wherein each training example comprises a sequence of data elements; using the collection of training examples to train a tree model that includes a plurality of paths of connected nodes that each represent a respective sequence of data elements from the collection of training examples by associating each path in the tree model that represents a sequence of data elements with a count feature that represents a number of times the sequences of data elements occurs in the collection of training examples; and performing a sequence modeling task or an agent control task using the trained tree model.