Transformer networks are gaining popularity as a high-accuracy alternative to recurrent neural networks. But they can run slowly when they’re applied to long sequences.
Transformer networks are gaining popularity as a high-accuracy alternative to recurrent neural networks. But they can run slowly when they’re applied to long sequences.
The technique known as dropout discourages neural networks from overfitting by deterring them from reliance on particular features. A new approach reorganizes the process to run efficiently on the chips that typically run neural network calculations.
If the world changes every second and you take a picture every 10 seconds, you won’t have enough pictures to observe the changes clearly, and storing a series of pictures won’t help. On the other hand, if you take a picture every tenth of a second, then storing a history will help model the world.
If the world changes every second and you take a picture every 10 seconds, you won’t have enough pictures to observe the changes clearly, and storing a series of pictures won’t help. On the other hand, if you take a picture every tenth of a second, then storing a history will help model the world.
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.
People who take driving lessons during daytime don’t need instruction in driving at night. They recognize that the difference doesn’t disturb their knowledge of how to drive. Similarly, a new reinforcement learning method manages superficial variations in the environment without re-training.
People who take driving lessons during daytime don’t need instruction in driving at night. They recognize that the difference doesn’t disturb their knowledge of how to drive. Similarly, a new reinforcement learning method manages superficial variations in the environment without re-training.
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.
Bias arises in machine learning when we fit an overly simple function to a more complex problem. A theoretical study shows that gradient descent itself may introduce such bias and render algorithms unable to fit data properly.
With the rise of AI-driven surveillance, anonymity is in fashion. Researchers are working on clothing that evades face recognition systems and designed a t-shirt that tricks a variety of object detection models into failing to spot people.
With the rise of AI-driven surveillance, anonymity is in fashion. Researchers are working on clothing that evades face recognition systems and designed a t-shirt that tricks a variety of object detection models into failing to spot people.
Bias arises in machine learning when we fit an overly simple function to a more complex problem. A theoretical study shows that gradient descent itself may introduce such bias and render algorithms unable to fit data properly.
Language models like Bert, Ernie, and Elmo have achieved spectacular results based on clever pre-training approaches. New research applies some of those Sesame Street lessons into image processing.
Language models like Bert, Ernie, and Elmo have achieved spectacular results based on clever pre-training approaches. New research applies some of those Sesame Street lessons into image processing.
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