You can now directly optimize for classification accuracy during training instead of using proxy losses, improving performance especially when trai...
This paper solves a long-standing problem in machine learning: how to optimize the zero-one loss (the metric that actually measures classification accuracy) using gradient descent. The authors create a smooth mathematical approximation that lets you backpropagate through this loss, which helps models train better on large batches of data.