New insights into training dynamics of deep classifiers

A new study from researchers at MIT and Brown University characterizes several properties that emerge during the training of deep classifiers, a type of artificial neural network commonly used for classification tasks such as image classification, sp

July 31, 23
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Scaling audio-visual learning without labels

“A larger portion of human knowledge is learned in a self-supervised way, because we don't always get supervision signals, and we want to enable the machine-learning model to have the same ability,” says Yuan Gong, an MIT postdoc in the Computer Scie

July 31, 23
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Computational model mimics humans’ ability to predict emotions

MIT neuroscientists have now designed a computational model that can predict other people’s emotions — including joy, gratitude, confusion, regret, and embarrassment — approximating human observers’ social intelligence. The model was designed to pred

July 31, 23
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Building resilience for the next supply chain disruption

Yet, organizations should not focus too much time prepping for another pandemic or trying to handicap the next disruption, says MIT supply chain resilience researcher Jim Rice. Instead, they should build resilience to protect against any threat.

July 31, 23
76 VIEWS
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New insights into training dynamics of deep classifiers

A new study from researchers at MIT and Brown University characterizes several properties that emerge during the training of deep classifiers, a type of artificial neural network commonly used for classification tasks such as image classification, sp

July 31, 23
75 VIEWS
0 LIKS

A simpler method for learning to control a robot

This technique could help an autonomous vehicle learn to compensate for slippery road conditions to avoid going into a skid, allow a robotic free-flyer to tow different objects in space, or enable a drone to closely follow a downhill skier despite be

July 27, 23
79 VIEWS
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July 27, 23

A simpler method for learning to control a robot

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