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Machine Learning Algorithms
Posted: Sep 14, 2021 | Reading Time: 1 Minutes
Massachusetts Institute of Technology researchers have developed a neural network that adapts to conditions on its own without the use of extra training. Dubbed “liquid” networks, the algorithms can change their equations to continuously adapt to new data as it’s encountered.
The new algorithm can be especially useful for applications that utilize any form of time series data processing, such as medical diagnostic applications, video processing, robot control, and natural language processing. According to the study’s lead author Ramin Hasani, time series data processes are “both ubiquitous and vital to our understanding the world.” Changes in time series data can be unpredictable and abrupt, but the ability to use the “liquid” neural network to analyze the changes in real time and learn and anticipate future behavior can increase the development of emerging technologies such as automated machinery or self-driving cars.
The behavior of most neural networks is fixed after an initial training phase, leaving them ill-equipped to adjust to changes that may arise in their data stream. Hasani says that his liquid network bypasses this issue, as the network is based on the processing pathways of the brain: its parameters are able to change over time on its own to adapt to alterations in the information that it processes.
The liquid neural network has excelled in a variety of tests, beating other time series algorithms by several percentage points in accurately predicting future values in datasets that ranged from atmospheric chemistry to traffic patterns. The network’s small size also enables it to complete tests without a steep cost. Hasani is planning to improve the network to get it ready for industrial application. "We have a provably more expressive neural network that is inspired by nature, but this is just the beginning of the process."
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