Injecting structure into neural networks enables learning functions that satisfy invariances with respect to subsets of inputs.

Pre-training has been widely adopted in deep learning to improve model performance, especially when the training data for a target task is limited.

Avila is a non-image dataset. A.3 Image classification on MNIST We perform the common per-pixel zero-mean unit-variance normalization as data preprocessing for MNIST. Pretraining Hyperparameter Settings: We train neural networks for 50 epochs on MNIST for obtaining pretrained models. The batch size is set to 1 and no weight decay is used, unless we specify them otherwise. As for other optimizer hyperparameters, we apply the default settings directly.

Stochastic gradients closely relate to both optimization and generalization of deep neural networks (DNNs).

By modeling robustness in terms of the condition number of the neural network, we argue that this loss of robustness is due to the exploding singular values of the low-rank weight matrices.

Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing. The state-of-the-art methods rely on priors of fitting local surfaces learned from normal supervision.

We use the PyTorch framework for our experiments. Similar to TD3, we implement our GRU-ODE in SAC. In this ablation study, we ask two questions in relation to numerical integration. Thus, simple numerical solvers are enough. We evaluate the time costs of different baselines on Walker-P environments.