An Adaptive Memory Multi-Batch L-BFGS Algorithm for Neural Network Training
This work provides an incremental improvement to L-BFGS optimization for researchers and practitioners training neural networks, particularly MLPs and CNNs.
The paper proposes a multi-batch L-BFGS algorithm called MB-AM that adaptively increases its trust in curvature information during neural network training. Empirically, MB-AM shows slightly faster convergence and, on average, achieves better solutions than the standard multi-batch L-BFGS on six benchmark problems for MLP and CNN models.
Motivated by the potential for parallel implementation of batch-based algorithms and the accelerated convergence achievable with approximated second order information a limited memory version of the BFGS algorithm has been receiving increasing attention in recent years for large neural network training problems. As the shape of the cost function is generally not quadratic and only becomes approximately quadratic in the vicinity of a minimum, the use of second order information by L-BFGS can be unreliable during the initial phase of training, i.e. when far from a minimum. Therefore, to control the influence of second order information as training progresses, we propose a multi-batch L-BFGS algorithm, namely MB-AM, that gradually increases its trust in the curvature information by implementing a progressive storage and use of curvature data through a development-based increase (dev-increase) scheme. Using six discriminative modelling benchmark problems we show empirically that MB-AM has slightly faster convergence and, on average, achieves better solutions than the standard multi-batch L-BFGS algorithm when training MLP and CNN models.