Efficiency
Attention to Rows and Columns: Altering Transformers' Self-Attention Mechanism for Greater Efficiency
A new approach alters transformers' self-attention mechanism to balance computational efficiency with performance on vision tasks.
Efficiency
Ensemble Models Simplified: New Machine Learning Research Simplifies Ensembles
A CLIP model whose weights were the mean of an ensemble of fine-tuned models performed as well as the ensemble and better than its best-performing constituent.
Efficiency
Ensemble Models Simplified: New Machine Learning Research Simplifies Ensembles
A CLIP model whose weights were the mean of an ensemble of fine-tuned models performed as well as the ensemble and better than its best-performing constituent.
Efficiency
Cutting the Carbon Cost of Training: A New Tool Helps NLP Models Lower Their Gas Emissions
You can reduce your model’s carbon emissions by being choosy about when and where you train it.
Efficiency
Cutting the Carbon Cost of Training: A New Tool Helps NLP Models Lower Their Gas Emissions
You can reduce your model’s carbon emissions by being choosy about when and where you train it.
Efficiency
Tradeoffs for Higher Accuracy: Data Augmentation Plus Weight Decay can Boost Some AI Models
Vision models can be improved by training them on several altered versions of the same image and also by encouraging their weights to be close to zero. Recent research showed that both can have adverse effects that may be difficult to detect.
Efficiency
Tradeoffs for Higher Accuracy: Data Augmentation Plus Weight Decay can Boost Some AI Models
Vision models can be improved by training them on several altered versions of the same image and also by encouraging their weights to be close to zero. Recent research showed that both can have adverse effects that may be difficult to detect.
Efficiency
Who Was That Masked Input? Pretraining Method Improves Computer Vision Performance
Researchers have shown that it’s possible to train a computer vision model effectively on around 66 percent of the pixels in each training image. New work used 25 percent, saving computation and boosting performance to boot.
Efficiency
Who Was That Masked Input? Pretraining Method Improves Computer Vision Performance
Researchers have shown that it’s possible to train a computer vision model effectively on around 66 percent of the pixels in each training image. New work used 25 percent, saving computation and boosting performance to boot.
Efficiency
High Accuracy at Low Power: An energy efficient method for computer vision
Equipment that relies on computer vision while unplugged — mobile phones, drones, satellites, autonomous cars — need power-efficient models. A new architecture set a record for accuracy per computation.
Efficiency
High Accuracy at Low Power: An energy efficient method for computer vision
Equipment that relies on computer vision while unplugged — mobile phones, drones, satellites, autonomous cars — need power-efficient models. A new architecture set a record for accuracy per computation.
Efficiency
AI Versus the Garbage Heap: How Amazon uses AI to cut waste.
Amazon reported long-term success using machine learning to shrink its environmental footprint. The online retailer developed a system that fuses product descriptions, images, and structured data to decide how an item should be packed for shipping.
Efficiency
More Learning With Less Memory: Training large language models using less memory.
Researchers discovered a new way to reduce memory requirements when training large machine learning models. Tim Dettmers and colleagues at University of Washington released 8-bit optimizers that store gradient statistics as 8-bit values, while maintaining the same accuracy.
Efficiency
AI Versus the Garbage Heap: How Amazon uses AI to cut waste.
Amazon reported long-term success using machine learning to shrink its environmental footprint. The online retailer developed a system that fuses product descriptions, images, and structured data to decide how an item should be packed for shipping.
Efficiency
More Learning With Less Memory: Training large language models using less memory.
Researchers discovered a new way to reduce memory requirements when training large machine learning models. Tim Dettmers and colleagues at University of Washington released 8-bit optimizers that store gradient statistics as 8-bit values, while maintaining the same accuracy.
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