SYSTEM AND METHOD FOR SELECTING COMPONENTS IN DESIGNING MACHINE LEARNING MODELS
Disclosed are example embodiments of systems and methods for selecting components for building graph-based learning machines. An example system for selecting components for building graph-based learning machines includes a reference learning machine, one or more test signals, and a component analyzer module. The component analyzer module is configured to analyze, using the one or more test signals, one or more component in the reference machine by ranking different components in the reference learning machine in terms of their efficiency and effectiveness.
1 . A system for selecting components for building graph-based learning machines, comprising:
a reference learning machine;
one or more test signals; and
a component analyzer configured to analyze, using the one or more test signals, one or more components in the reference learning machine by ranking different components in the reference learning machine in terms of the one or more components' efficiency and effectiveness.
2 . The system of claim 1 , wherein one or more analyzed and ranked component in the reference learning machine are replaced with one or more components that are more efficient or more effective.
3 . The system of claim 2 , further comprises a learning machine builder configured to generate a new learning machine after the reference learning machine includes the one or more components that are more efficient or more effective.
4 . The system of claim 1 , further comprising a graph component comprising at least one of a node, a set of nodes, or a group of smaller graph components which, given input data, produces a set of outputs.
5 . The system of claim 1 , further comprising a combination of graph components that model data.
6 . The system of claim 5 , wherein the graph components are included in a pool of components and wherein the system further defines new components and adds them to the pool of components.
7 . The system of claim 1 , wherein the component analyzer or graph component analyzer measures an effectiveness of each component in the reference learning machine in terms of modeling performance and the component analyzer, and wherein a graph component analyzer ranks the components based on the one or more components' effectiveness.
8 . The system of claim 1 , wherein the component analyzer or graph component analyzer evaluates an efficiency of each component in the reference learning machine in terms of computational complexity given the one or more components' effectiveness.
9 . The system of claim 1 , wherein the component analyzer or graph component analyzer evaluates a performance of graph components listed in a pool of components to be added into the reference learning machine to improve a modeling accuracy and reduce a computational complexity of a new generated learning machine.
10 . The system of claim 1 , wherein a learning machine builder generates a new learning machine with improved efficiency and modeling accuracy compared to the reference learning machine by identifying the best graph components to be replaced in the reference learning machine and a list of potential components.
11 . The system of claim 1 , wherein an efficiency of a model may be defined as an inference speed or a memory footprint to process an input signal.
12 . The system of claim 1 , wherein a learning machine builder generates a new graph component from scratch which is not in a pool of components to improve a performance of the reference learning machine.
13 . The system of claim 1 , wherein a performance of the reference learning machine is measured in terms of functional accuracy or inference speed.
14 . The system of claim 1 , wherein a learning machine builder identifies a numbers of graph components and layers in the reference learning machine, given specific performance.
15 . The system of claim 1 , wherein a learning machine builder builds a new learning machine with an optimized number of components.
16 . The system of claim 1 , wherein a learning machine builder tunes and re-designs the reference learning machine to provide better performance given learning machine complexity.
17 . The system of claim 1 , configured to design a new learning machine for an image classification application.
18 . The system of claim 1 , wherein the system generates a new learning machine to create a speech recognition system.
19 . The system of claim 1 , wherein an image classifier generates from the reference learning machine may receive an image of a handwritten digit into a network and make a decision on what class the image belongs to.
20 . A method of selecting components for building a graph-based learning machine, the method comprising:
observing behavior of a component in a reference learning machine;
ranking the component in the reference learning machine in terms of the component's efficiency and effectiveness to evaluate the effectiveness of the component;
identifying issues in the component of the reference learning machine; and
generating the graph-based learning machine based on ranking and the issues to design a better learning machine.