Categories: FAANG

RepCNN: Micro-Sized, Mighty Models for Wakeword Detection

Always-on machine learning models require a very low memory and compute footprint. Their restricted parameter count limits the model’s capacity to learn, and the effectiveness of the usual training algorithms to find the best parameters. Here we show that a small convolutional model can be better trained by first refactoring its computation into a larger redundant multi-branched architecture. Then, for inference, we algebraically re-parameterize the trained model into the single-branched form with fewer parameters for a lower memory footprint and compute cost. Using this technique, we show…
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I’ll definitely try this one out later… oh… it’s already obsolete

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20 hours ago

From hallucinations to hardware: Lessons from a real-world computer vision project gone sideways

What we tried, what didn't work and how a combination of approaches eventually helped us…

21 hours ago

OpenAI Loses 4 Key Researchers to Meta

Mark Zuckerberg has been working to poach talent from rival labs for his new superintelligence…

21 hours ago

Evaluating Long Range Dependency Handling in Code Generation LLMs

As language models support larger and larger context sizes, evaluating their ability to make effective…

2 days ago

AWS costs estimation using Amazon Q CLI and AWS Cost Analysis MCP

Managing and optimizing AWS infrastructure costs is a critical challenge for organizations of all sizes.…

2 days ago