Model with state, multifunction model 이란? 한글로 설명해줘
Generated on 8/12/2024
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Model with State
"Model with state" refers to machine learning models that retain information across different runs or inputs. Unlike stateless models, which process each input independently, stateful models maintain a history of previous inputs. This is particularly useful in scenarios where the model needs to remember past information to make accurate predictions, such as in sequence data processing.
In the context of Apple's Core ML, stateful models are now supported, allowing the model to automatically manage state tensors without needing to define them as inputs or outputs. This leads to better performance and efficiency, as the model can update states in place. An example of a stateful model is an accumulator that keeps track of the summation of all historical inputs.
Multifunction Model
A multifunction model is a model that supports multiple functions, allowing it to perform various tasks without needing to adjust its weights. This is achieved by using adapters, which are small modules embedded into an existing network. These adapters can be trained for different tasks, enabling a single base model to be shared across multiple adapters. This approach is efficient and allows for extending the functionality of a large pre-trained model.
In Core ML, multifunction models can be created by merging different functions, such as a classifier and a regressor, into a single model. This allows for efficient deployment and use of models for multiple tasks.
For more details, you can refer to the session Bring your machine learning and AI models to Apple silicon (26:24) for multifunction models and Deploy machine learning and AI models on-device with Core ML (12:33) for multifunction models.
Bring your machine learning and AI models to Apple silicon
Learn how to optimize your machine learning and AI models to leverage the power of Apple silicon. Review model conversion workflows to prepare your models for on-device deployment. Understand model compression techniques that are compatible with Apple silicon, and at what stages in your model deployment workflow you can apply them. We’ll also explore the tradeoffs between storage size, latency, power usage and accuracy.
Deploy machine learning and AI models on-device with Core ML
Learn new ways to optimize speed and memory performance when you convert and run machine learning and AI models through Core ML. We’ll cover new options for model representations, performance insights, execution, and model stitching which can be used together to create compelling and private on-device experiences.