Semi-supervised learning — a set of training techniques that use a small number of labeled examples and a large number of unlabeled examples — typically treats all unlabeled examples the same way. But some examples are more useful for learning than others.
Semi-supervised learning — a set of training techniques that use a small number of labeled examples and a large number of unlabeled examples — typically treats all unlabeled examples the same way. But some examples are more useful for learning than others.
From server to smartphone, devices with less processing speed and memory require smaller networks. Instead of building and training separate models to run on a variety of hardware, a new approach trains a single network that can be adapted to any device.
A growing body of literature shows that some steps in AI’s forward march may actually move sideways. A new study questions advances in metric learning.
A growing body of literature shows that some steps in AI’s forward march may actually move sideways. A new study questions advances in metric learning.
From server to smartphone, devices with less processing speed and memory require smaller networks. Instead of building and training separate models to run on a variety of hardware, a new approach trains a single network that can be adapted to any device.
Natural language processing lately has come to resemble an arms race, as the big AI companies build models that encompass ever larger numbers of parameters. Microsoft recently held the record — but not for long.
Natural language processing lately has come to resemble an arms race, as the big AI companies build models that encompass ever larger numbers of parameters. Microsoft recently held the record — but not for long.
Machine learning researchers report better and better results, but some of that progress may be illusory. Some models that appear to set a new state of the art haven’t been compared properly to their predecessors, Science News reports based on several published surveys.
Reinforcement learning can beat humans at video games, but humans are better at coming up with strategies to master more complex tasks. New work enables neural networks to connect the dots.
Reinforcement learning can beat humans at video games, but humans are better at coming up with strategies to master more complex tasks. New work enables neural networks to connect the dots.
Machine learning researchers report better and better results, but some of that progress may be illusory. Some models that appear to set a new state of the art haven’t been compared properly to their predecessors, Science News reports based on several published surveys.
Wait a minute — we added training data, and our model’s performance got worse?! New research offers a way to avoid so-called double descent.
Given real-world constraints on memory and processing time, images are often downsampled before they’re fed into a neural network. But the process removes fine details, and that degrades accuracy. A new technique squeezes images with less compromise.
Wait a minute — we added training data, and our model’s performance got worse?! New research offers a way to avoid so-called double descent.
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