In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[EX Q]: Structured tabular data is the most commonly used form of data in industry according to a Kaggle ML and DS Survey. Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for classification tasks on tabular data. The recent work of Super Characters method using two-dimensional word embeddings achieved state-of-the-art results in text classification tasks, showcasing the promise of this new approach. In this paper, we propose the SuperTML method, which borrows the idea of Super Characters method and two-dimensional embeddings to address the problem of classification on tabular data. For each input of tabular data, the features are first projected into two-dimensional embeddings like an image, and then this image is fed into fine-tuned ImageNet CNN models for classification. Experimental results have shown that the proposed SuperTML method have achieved state-of-the-art results on both large and small datasets.
[EX A]: Deep learning on structured tabular data using two-dimensional word embedding with fine-tuned ImageNet pre-trained CNN model.

[EX Q]: We show that information about whether a neural network's output will be correct or incorrect is present in the outputs of the network's intermediate layers. To demonstrate this effect, we train a new "meta" network to predict from either the final output of the underlying "base" network or the output of one of the base network's intermediate layers whether the base network will be correct or incorrect for a particular input. We find that, over a wide range of tasks and base networks, the meta network can achieve accuracies ranging from 65% - 85% in making this determination.

[EX A]: Information about whether a neural network's output will be correct or incorrect is somewhat present in the outputs of the network's intermediate layers.

[EX Q]: Despite the remarkable performance of deep neural networks (DNNs) on various tasks, they are susceptible to adversarial perturbations which makes it difficult to deploy them in real-world safety-critical applications. In this paper, we aim to obtain robust networks by sparsifying DNN's latent features sensitive to adversarial perturbation. Specifically, we define vulnerability at the latent feature space and then propose a Bayesian framework to prioritize/prune features based on their contribution to both the original and adversarial loss. We also suggest regularizing the features' vulnerability during training to improve robustness further. While such network sparsification has been primarily studied in the literature for computational efficiency and regularization effect of DNNs, we confirm that it is also useful to design a defense mechanism through quantitative evaluation and qualitative analysis. We validate our method, \emph{Adversarial Neural Pruning (ANP)} on multiple benchmark datasets, which results in an improvement in test accuracy and leads to state-of-the-art robustness. ANP also tackles the practical problem of obtaining sparse and robust networks at the same time, which could be crucial to ensure adversarial robustness on lightweight networks deployed to computation and memory-limited devices.
[EX A]:
We propose a novel method for suppressing the vulnerability of latent feature space to achieve robust and compact networks.