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US 12,182,707 B2Granted
System and method for the discovering efficient random neural networks
AssigneeCarnegie Mellon University
InventorsMarios Savvides, Thanh Hai Phan, Zhiqiang Shen, Akshay Chawla
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Abstract
Disclosed herein is a system and method for novel neural architecture search using a random graph network backbone to facilitate the creation of an efficient network structure. The method utilizes reinforcement learning algorithms to build a complex relationship between intra-connections (i.e., links between blocks in a random graph network) and extra-connections (i.e., links among blocks across the random graphs network) for discovering an efficient random neural architecture.
Claims12
A method for optimizing a graph network representing a neural network comprising:
- training the graph network using a deep deterministic policy gradient, resulting in a chain of random graph blocks, each random graph block defining a state of the neural network;
- wherein the deep deterministic policy gradient defines an actor network to propose an action based on the state, the action comprising parameters defining a number of nodes in each random graph block and both intra-block connections and extra-block connections;
- and
- wherein the deep deterministic policy gradient defines a critic network that evaluates the proposed actions of the actor function.
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