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]: Training methods for deep networks are primarily variants on stochastic gradient descent.   Techniques that use (approximate) second-order information are rarely used because of the computational cost and noise associated with those approaches in deep learning contexts.   However, in this paper, we show how feedforward deep networks exhibit a low-rank derivative structure.   This low-rank structure makes it possible to use second-order information without needing approximations and without incurring a significantly greater computational cost than gradient descent.   To demonstrate this capability, we implement Cubic Regularization (CR) on a feedforward deep network with stochastic gradient descent and two of its variants.   There, we use CR to calculate learning rates on a per-iteration basis while training on the MNIST and CIFAR-10 datasets.   CR proved particularly successful in escaping plateau regions of the objective function.   We also found that this approach requires less problem-specific information (e.g. an optimal initial learning rate) than other first-order methods in order to perform well.
[EX A]: We show that deep learning network derivatives have a low-rank structure, and this structure allows us to use second-order derivative information to calculate learning rates adaptively and in a computationally feasible manner.

[EX Q]: Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively. This paper aims to establish formal connections between GANs and VAEs through a new formulation of them. We interpret sample generation in GANs as performing posterior inference, and show that GANs and VAEs involve minimizing KL divergences of respective posterior and inference distributions with opposite directions, extending the two learning phases of classic wake-sleep algorithm, respectively. The unified view provides a powerful tool to analyze a diverse set of existing model variants, and enables to transfer techniques across research lines in a principled way. For example, we apply the importance weighting method in VAE literatures for improved GAN learning, and enhance VAEs with an adversarial mechanism that leverages generated samples. Experiments show generality and effectiveness of the transfered techniques.
[EX A]: A unified statistical view of the broad class of deep generative models 

[EX Q]: Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems,
 including agents that can move with skill and agility through their environment. 
 An open problem in this setting is that of developing good strategies for integrating or merging policies
 for multiple skills, where each individual skill is a specialist in a specific skill and its associated state distribution. 
 We extend policy distillation methods to the continuous action setting and leverage this technique to combine \expert policies,
 as evaluated in the domain of simulated bipedal locomotion across different classes of terrain.
 We also introduce an input injection method for augmenting an existing policy network to exploit new input features.
 Lastly, our method uses transfer learning to assist in the efficient acquisition of new skills.
 The combination of these methods allows a policy to be incrementally augmented with new skills.
 We compare our progressive learning and integration via distillation (PLAID) method
 against three alternative baselines.
[EX A]:
A continual learning method that uses distillation to combine expert policies and transfer learning to accelerate learning new skills.