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.

Example Input: We present a new approach to assessing the robustness of neural networks based on estimating the proportion of inputs for which a property is violated. Specifically, we estimate the probability of the event that the property is violated under an input model. Our approach critically varies from the formal verification framework in that when the property can be violated, it provides an informative notion of how robust the network is, rather than just the conventional assertion that the network is not verifiable. Furthermore, it provides an ability to scale to larger networks than formal verification approaches. Though the framework still provides a formal guarantee of satisfiability whenever it successfully finds one or more violations, these advantages do come at the cost of only providing a statistical estimate of unsatisfiability whenever no violation is found. Key to the practical success of our approach is an adaptation of multi-level splitting, a Monte Carlo approach for estimating the probability of rare events, to our statistical robustness framework. We demonstrate that our approach is able to emulate formal verification procedures on benchmark problems, while scaling to larger networks and providing reliable additional information in the form of accurate estimates of the violation probability.
Example Output: We introduce a statistical approach to assessing neural network robustness that provides an informative notion of how robust a network is, rather than just the conventional binary assertion of whether or not of property is violated.

Example Input: In recent years several adversarial attacks and defenses have been proposed. Often seemingly robust models turn out to be non-robust when more sophisticated attacks are used. One way out of this dilemma are provable robustness guarantees. While provably robust models for specific $l_p$-perturbation models have been developed, we show that they do not come with any guarantee against other $l_q$-perturbations. We propose a new regularization scheme, MMR-Universal, for ReLU networks which enforces robustness wrt $l_1$- \textit{and} $l_\infty$-perturbations and show how that leads to the first provably robust models wrt any $l_p$-norm for $p\geq 1$.
Example Output: We introduce a method to train models with provable robustness wrt all the $l_p$-norms for $p\geq 1$ simultaneously.

Example Input: Recent advances in deep learning techniques has shown the usefulness of the deep neural networks in extracting features required to perform the task at hand.
 However, these features learnt are in particular helpful only for the initial task. This is due to the fact that the features learnt are very task specific and does not capture the most general and task agnostic features of the input.
 In fact the way humans are seen to learn is by disentangling features which task agnostic. This indicates that leaning task agnostic features by disentangling only the most informative features from the input data.
 Recently Variational Auto-Encoders (VAEs) have shown to be the de-facto models to capture the latent variables in a generative sense.
 As these latent features can be represented as continuous and/or discrete variables, this indicates us to use VAE with a mixture of continuous and discrete variables for the latent space.
 We achieve this by performing our experiments using a modified version of joint-vae to learn the disentangled features.

Example Output:
Mixture Model for Neural Disentanglement