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Found 5 papers, 0 papers with code
Modular, Hierarchical Machine Learning for Sequential Goal Completion
Instead of a monolithic ANN, a modular ML component would be 1) independently optimizable (task-agnostic) and 2) arbitrarily reconfigurable with other ML modules.
Assembling Modular, Hierarchical Cognitive Map Learners with Hyperdimensional Computing
Cognitive map learners (CML) are a collection of separate yet collaboratively trained single-layer artificial neural networks (matrices), which navigate an abstract graph by learning internal representations of the node states, edge actions, and edge action availabilities.
Modularizing and Assembling Cognitive Map Learners via Hyperdimensional Computing
In so doing, graph knowledge (CML) was segregated from target node selection (HDC), allowing each ML approach to be trained independently.
An FPGA Implementation of a Time Delay Reservoir Using Stochastic Logic
This paper presents and demonstrates a stochastic logic time delay reservoir design in FPGA hardware.
Reservoir Computing and Extreme Learning Machines using Pairs of Cellular Automata Rules
A framework for implementing reservoir computing (RC) and extreme learning machines (ELMs), two types of artificial neural networks, based on 1D elementary Cellular Automata (CA) is presented, in which two separate CA rules explicitly implement the minimum computational requirements of the reservoir layer: hyperdimensional projection and short-term memory.
