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Adaptive pooling operators for multiple instance learning

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autopool

Adaptive pooling operators for Multiple Instance Learning (documentation).

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AutoPool is an adaptive (trainable) pooling operator which smoothly interpolates between common pooling operators, such as min-, max-, or average-pooling, automatically adapting to the characteristics of the data.

AutoPool can be readily applied to any differentiable model for time-series label prediction. AutoPool is presented in the following paper, where it is evaluated in conjunction with convolutional neural networks for Sound Event Detection:

Adaptive pooling operators for weakly labeled sound event detection
Brian Mcfee, Justin Salamon, and Juan Pablo Bello
IEEE Transactions on Audio, Speech and Language Processing, In press, 2018.

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