Getting high accuracy out of a classifier trained on a small number of examples is tricky. You might train the model on several large-scale datasets prior to few-shot training, but what if the few-shot dataset includes novel classes? A new method performs well even in that case.
Getting high accuracy out of a classifier trained on a small number of examples is tricky. You might train the model on several large-scale datasets prior to few-shot training, but what if the few-shot dataset includes novel classes? A new method performs well even in that case.
A neural network wrote the blueprint for upcoming computer chips that will accelerate deep learning itself. Google engineers used a reinforcement learning system to arrange the billions of minuscule transistors in an upcoming version of its Tensor Processing Unit (TPU) chips.
A neural network wrote the blueprint for upcoming computer chips that will accelerate deep learning itself. Google engineers used a reinforcement learning system to arrange the billions of minuscule transistors in an upcoming version of its Tensor Processing Unit (TPU) chips.
Deploying models for practical use is an industrial concern that generally goes unaddressed in research. As a result, publications on the subject tend to come from the major AI companies.
Deploying models for practical use is an industrial concern that generally goes unaddressed in research. As a result, publications on the subject tend to come from the major AI companies.
Craig Wiley has journeyed from the hand-deployed models of yore to the pinnacle of automated AI. Today, as chief product manager of Google Cloud’s AI services, he’s making advanced tools and processes available to anyone with a credit card.
Craig Wiley has journeyed from the hand-deployed models of yore to the pinnacle of automated AI. Today, as chief product manager of Google Cloud’s AI services, he’s making advanced tools and processes available to anyone with a credit card.
A new study suggests tactics for machine learning engineers to cut their carbon emissions. Led by David Patterson, researchers at Google and UC Berkeley found that AI developers can shrink a model’s carbon footprint a thousand-fold by streamlining architecture...
A new study suggests tactics for machine learning engineers to cut their carbon emissions. Led by David Patterson, researchers at Google and UC Berkeley found that AI developers can shrink a model’s carbon footprint a thousand-fold by streamlining architecture...
In some animated games, different characters can perform the same actions — say, walking, jumping, or casting spells. A new system learned from unlabeled data to transfer such motions from one character to another.
In some animated games, different characters can perform the same actions — say, walking, jumping, or casting spells. A new system learned from unlabeled data to transfer such motions from one character to another.
A new system combines verbal descriptions and crude lines to visualize complex scenes. Google researchers led by Jing Yu Koh proposed Tag-Retrieve-Compose-Synthesize (TReCS), a system that generates photorealistic images by describing what they want to see while mousing around on a blank screen.
A new system combines verbal descriptions and crude lines to visualize complex scenes. Google researchers led by Jing Yu Koh proposed Tag-Retrieve-Compose-Synthesize (TReCS), a system that generates photorealistic images by describing what they want to see while mousing around on a blank screen.
Self-attention is a key element in state-of-the-art language models, but it struggles to process images because its memory requirement rises rapidly with the size of the input. New research addresses the issue with a simple twist on a convolutional neural network.
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