Optimizing Non-decomposable Measures with Deep Networks
This addresses the challenge of optimizing task-specific metrics in deep learning, offering a more efficient approach for practitioners, though it is incremental as it builds on existing gradient frameworks.
The paper tackles the problem of training deep neural networks directly with non-decomposable performance measures like F-measure and Kullback-Leibler divergence, resulting in faster convergence, reduced training time, and improved performance on benchmark datasets compared to traditional methods.
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.