Dec 7, 2019 · We present an efficient, theoretically sound, and general method for differentiating rank-based metrics with mini-batch gradient descent.
We present an efficient, theoretically sound, and general method for differentiating rank-based metrics with mini-batch gradient descent. In addition, we.
#blackboxbackprop allows to differentiate through the ranking function out of the box. This, along with a few other tricks, allows for efficient optimization of ...
We present an efficient, theoretically sound, and general method for differentiating rank-based metrics with mini-batch gradient descent. In addition, we.
We apply this insight to optimizing mean average precision and recall in object detection and retrieval tasks, where we achieve comparable results to state-of- ...
We present an efficient, theoretically sound, and general method for differentiating rank-based metrics with mini-batch gradient descent. In addition, we ...
In all experiments we used the ADAM optimizer with a weight decay value of 4 × 10−4 and batch size 128. All experiments ran at most 80 epochs with a ...
We present an efficient, theoretically sound, and general method for differentiating rank-based metrics with mini-batch gradient descent. In addition, we ...
Papertalk is an open-source platform where scientists share video presentations about their newest scientific results - and watch, like + discuss them.
Duration: 5:00
Posted: May 18, 2020
Posted: May 18, 2020