Optimizing Non-decomposable Measures with Deep Networks

TL;DR

This paper introduces algorithms for directly training deep neural networks on complex, task-specific performance measures like F-measure and KL divergence, leading to faster, more stable training.

Contribution

It presents novel algorithms that optimize non-decomposable, structured loss functions directly within deep learning frameworks, improving convergence and efficiency.

Findings

Faster and more stable convergence across datasets.

Reduced training time and sample requirements.

Outperforms traditional and recent task-specific training methods.

Abstract

We present a class of algorithms capable of directly training deep neural networks with respect to large families of task-specific performance measures such as the F-measure and the Kullback-Leibler divergence that are structured and non-decomposable. This presents a departure from standard deep learning techniques that typically use squared or cross-entropy loss functions (that are decomposable) to train neural networks. We demonstrate that directly training with task-specific loss functions yields much faster and more stable convergence across problems and datasets. Our proposed algorithms and implementations have several novel features including (i) convergence to first order stationary points despite optimizing complex objective functions; (ii) use of fewer training samples to achieve a desired level of convergence, (iii) a substantial reduction in training time, and (iv) a seamless integration of our implementation into existing symbolic gradient frameworks. We implement our techniques on a variety of deep architectures including multi-layer perceptrons and recurrent neural networks and show that on a variety of benchmark and real data sets, our algorithms outperform traditional approaches to training deep networks, as well as some recent approaches to task-specific training of neural networks.