On Tuning Neural ODE for Stability, Consistency and Faster Convergence (2312.01657v1)

Abstract: 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.

• The paper demonstrates that a NAG-based ODE solver significantly improves stability, consistency, and convergence speed in Neural ODEs.
• It details how the adjoint sensitivity method ensures memory efficiency by maintaining a constant cost regardless of network depth.
• Empirical evaluations reveal that Neural ODEs with the new solver achieve competitive or superior performance compared to ResNets on tasks such as MNIST classification.

Neural Ordinary Differential Equations (Neural ODEs) have garnered significant attention in machine learning for their ability to model continuous transformations and save computational memory. However, one of the challenges with Neural ODEs is finding an appropriate ODE solver that ensures the model trains effectively and can solve differential equations consistently and stably. To address these issues, a new paper has focused on the utilization of a Nesterov’s accelerated gradient (NAG) based ODE-solver that can be tuned for stability, consistency, and faster convergence.

Neural ODEs offer a memory-efficient alternative to traditional neural network architectures, such as Residual Networks (ResNets), sharing similar performance while using significantly less memory. Memory efficiency is primarily achieved through an application of the adjoint sensitivity method that computes gradients of the loss function with respect to the weights of the ODE network. The adjoint method maintains a constant memory cost function of depth, where depth refers to the number of neural network layers or, in the context of ODEs, how far we've integrated the differential equation forward in time. However, the time complexity advantage of Neural ODEs over ResNets isn't always evident due to the performance of the numerical ODE solvers nestled within Neural ODEs. These solvers can sometimes have drawbacks associated with slower convergence or a lack of convergence.

The proposed approach in the paper uses a first-order Nesterov’s accelerated gradient (NAG) based ODE-solver. This solver is proven to be tuned vis-a-vis ensuring faster model training and better or comparable model performance against other fixed-step explicit ODE solvers and discrete depth models such as ResNets. Notably, the paper demonstrates that by leveraging a NAG-based solver, a Neural ODE can outperform some traditional neural network models, including ResNets, in terms of training time and model performance on various machine learning tasks.

This paper undertakes empirical evaluations across multiple tasks, including supervised classification, time-series modeling, and density estimation. For example, in the classification task using the MNIST dataset, which consists of hand-written digits, the Neural ODE with the NAG-based solver achieved better or at least comparable classification accuracy to other well-known techniques.

In conclusion, the paper's results highlight the potential of a NAG-based ODE solver in improving the training of Neural ODEs. It opens up new avenues for further research, such as exploring the optimal selection of ODE solvers for different tasks and the potential for combining regularization techniques with a NAG-based solver for enhanced performance.