Bayesian inference for the truncated GP can be achieved, in many cases with substantially lower computation than would be required using MCMC methods.

Stochastic gradient descent (SGD) is a cornerstone of machine learning.

This implies that adversarial training can serve as a principled defense against delusive attacks. Thus, the test accuracy decreased by delusive attacks can be largely recovered by adversarial training.

Table 2 presents the numerical results for the ablation study in Section 4.2. The results of our main method in Section 4.1 is reported in column Main. Table 3 provides additional ablation study on several building blocks of our main method. T est denotes the variant of using the estimated reward function as the test function when We see that changing the proposed JSD regularizer in Section 3.2 to the KL-dual-based regularizers Changing the implicit policy to the Gaussian policy generally leads to worse performance. The performance difference is especially significant on the Maze2D and Adroit datasets.

However, there has been a recent move towards non-parametric neural LMs (Guu et al., 2018; Khandelwal et al., 2020b) that generate sentences by first selecting examples from an external datastore. For instance, Khandelwal et al. (2020b) model the token-level probability at test time by interpolating the language model with a kNN distribution from the nearest context-token pairs in the datastore, while Guu et al. (2018) store external memories on sentence level and feature a

The linear mapping in deep neural networks (DNNs) are mostly realized in the form of fully connected (FC) or convolutional (CONV) layers.

Computing the Jacobian of the solution of an optimization problem is a central problem in machine learning, with applications in hyperparameter optimization, meta-learning, optimization as a layer, and dataset distillation, to name a few.