On Tuning Neural ODE for Stability, Consistency and Faster Convergence
This addresses a key bottleneck for researchers and practitioners using Neural ODEs, offering an incremental improvement to enhance reliability and efficiency.
The paper tackled stability, consistency, and convergence issues in Neural ODEs by proposing a first-order Nesterov's accelerated gradient-based ODE-solver, resulting in faster training and better or comparable performance in tasks like classification, density estimation, and time-series modeling.
Neural-ODE parameterize a differential equation using continuous depth neural network and solve it using numerical ODE-integrator. These models offer a constant memory cost compared to models with discrete sequence of hidden layers in which memory cost increases linearly with the number of layers. In addition to memory efficiency, other benefits of neural-ode include adaptability of evaluation approach to input, and flexibility to choose numerical precision or fast training. However, despite having all these benefits, it still has some limitations. We identify the ODE-integrator (also called ODE-solver) as the weakest link in the chain as it may have stability, consistency and convergence (CCS) issues and may suffer from slower convergence or may not converge at all. We propose a first-order Nesterov's accelerated gradient (NAG) based ODE-solver which is proven to be tuned vis-a-vis CCS conditions. We empirically demonstrate the efficacy of our approach by training faster, while achieving better or comparable performance against neural-ode employing other fixed-step explicit ODE-solvers as well discrete depth models such as ResNet in three different tasks including supervised classification, density estimation, and time-series modelling.