Efficiency
Moderating the ML Roller Coaster: A technique to avoid double descent in AI
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.
Efficiency
Optimize Your Training Parameters: Research on finding a neural net's optimal batch size
Last week we reported on a formula to determine model width and dataset size for optimal performance. A new paper contributes equations that optimize some training parameters.
Efficiency
Optimize Your Training Parameters: Research on finding a neural net's optimal batch size
Last week we reported on a formula to determine model width and dataset size for optimal performance. A new paper contributes equations that optimize some training parameters.
Efficiency
Transformers Transformed: Research improves transformer efficiency with Reformer.
Transformer networks have revolutionized natural language processing, but they hog processor cycles and memory. New research demonstrates a more frugal variation.
Efficiency
Transformers Transformed: Research improves transformer efficiency with Reformer.
Transformer networks have revolutionized natural language processing, but they hog processor cycles and memory. New research demonstrates a more frugal variation.
Efficiency
Easy on the Eyes: More accurate object detection with EfficientDet
Researchers aiming to increase accuracy in object detection generally enlarge the network, but that approach also boosts computational cost. A novel architecture sets a new state of the art in accuracy while cutting the compute cycles required.
Efficiency
Easy on the Eyes: More accurate object detection with EfficientDet
Researchers aiming to increase accuracy in object detection generally enlarge the network, but that approach also boosts computational cost. A novel architecture sets a new state of the art in accuracy while cutting the compute cycles required.
Efficiency
Convolution Revolution
Looking at images, people see outlines before the details within them. A replacement for the traditional convolutional layer decomposes images based on this distinction between coarse and fine features.
Efficiency
Convolution Revolution
Looking at images, people see outlines before the details within them. A replacement for the traditional convolutional layer decomposes images based on this distinction between coarse and fine features.
Efficiency
High Accuracy, Low Compute
As neural networks have become more accurate, they’ve also ballooned in size and computational cost. That makes many state-of-the-art models impractical to run on phones and potentially smaller, less powerful devices.
Efficiency
High Accuracy, Low Compute
As neural networks have become more accurate, they’ve also ballooned in size and computational cost. That makes many state-of-the-art models impractical to run on phones and potentially smaller, less powerful devices.
Efficiency
Tesla Bets on Slim Neural Nets
Elon Musk has promised a fleet of autonomous Tesla taxis by 2020. The company reportedly purchased a computer vision startup to help meet that goal. Tesla acquired DeepScale, a Silicon Valley startup that rocesses computer vision on low-power electronics.
Efficiency
Tesla Bets on Slim Neural Nets
Elon Musk has promised a fleet of autonomous Tesla taxis by 2020. The company reportedly purchased a computer vision startup to help meet that goal. Tesla acquired DeepScale, a Silicon Valley startup that rocesses computer vision on low-power electronics.
Efficiency
Honey, I Shrunk the Network!
Deep learning models can be unwieldy and often impractical to run on smaller devices without major modification. Researchers at Facebook AI Research found a way to compress neural networks with minimal sacrifice in accuracy.
Efficiency
Honey, I Shrunk the Network!
Deep learning models can be unwieldy and often impractical to run on smaller devices without major modification. Researchers at Facebook AI Research found a way to compress neural networks with minimal sacrifice in accuracy.
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