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A liquid neural network that learns on the job, not just during its training phase

MIT researchers have developed neural “liquid” network that varies its equations parameters, enhancing its ability to analyze time series data.

Image: Jose-Luis Olivares, MIT

 

Exploration Highlights

 

  • The new type of neural network could aid decision making in autonomous driving and medical diagnosis.
  • These flexible algorithms, dubbed “liquid” networks, change their underlying equations to continuously adapt to new data inputs. The advance could aid decision making based on data streams that change over time, including those involved in medical diagnosis and autonomous driving.
  • “This is a way forward for the future of robot control, natural language processing, video processing — any form of time series data processing,” says Ramin Hasani
  • Hasani designed a neural network that can adapt to the variability of real-world systems.
  • This flexibility is key. Most neural networks’ behavior is fixed after the training phase, which means they’re bad at adjusting to changes in the incoming data stream. Hasani says the fluidity of his “liquid” network makes it more resilient to unexpected or noisy data, like if heavy rain obscures the view of a camera on a self-driving car. “So, it’s more robust,” he says.
  • Hasani’s network excelled in a battery of tests. It edged out other state-of-the-art time series algorithms by a few percentage points in accurately predicting future values in datasets, ranging from atmospheric chemistry to traffic patterns.
  • “We think this kind of network could be a key element of future intelligence systems.”

 

Original Article: “Liquid” machine-learning system adapts to changing conditions

More from: Massachusetts Institute of Technology on Innovation2

 

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