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]: Deep neural networks are complex non-linear models used as predictive analytics tool and have demonstrated state-of-the-art performance on many classification tasks. However, they have no inherent capability to recognize when their predictions might go wrong. There have been several efforts in the recent past to detect natural errors i.e. misclassified inputs but these mechanisms pose additional energy requirements. To address this issue, we present a novel post-hoc framework to detect natural errors in an energy efficient way. We achieve this by appending relevant features based linear classifiers per class referred as Relevant features based Auxiliary Cells (RACs). The proposed technique makes use of the consensus between RACs appended at few selected hidden layers to distinguish the correctly classified inputs from misclassified inputs. The combined confidence of RACs is utilized to determine if classification should terminate at an early stage. We demonstrate the effectiveness of our technique on various image classification datasets such as CIFAR10, CIFAR100 and Tiny-ImageNet. Our results show that for CIFAR100 dataset trained on VGG16 network, RACs can detect 46% of the misclassified examples along with 12% reduction in energy compared to the baseline network while 69% of the examples are correctly classified.

[EX A]: Improve the robustness and energy efficiency of a deep neural network using the hidden representations.

[EX Q]: This paper proposes variational domain adaptation, a unified, scalable, simple framework for learning multiple distributions through variational inference. Unlike the existing methods on domain transfer through deep generative models, such as StarGAN (Choi et al., 2017) and UFDN (Liu et al., 2018), the variational domain adaptation has three advantages. Firstly, the samples from the target are not required. Instead, the framework requires one known source as a prior $p(x)$ and binary discriminators, $p(\mathcal{D}_i|x)$, discriminating the target domain $\mathcal{D}_i$ from others. Consequently, the framework regards a target as a posterior that can be explicitly formulated through the Bayesian inference, $p(x|\mathcal{D}_i) \propto p(\mathcal{D}_i|x)p(x)$, as exhibited by a further proposed model of dual variational autoencoder (DualVAE). Secondly, the framework is scablable to large-scale domains. As well as VAE encodes a sample $x$ as a mode on a latent space: $\mu(x) \in \mathcal{Z}$, DualVAE encodes a domain $\mathcal{D}_i$ as a mode on the dual latent space $\mu^*(\mathcal{D}_i) \in \mathcal{Z}^*$, named domain embedding. It reformulates the posterior with a natural paring $\langle,
angle: \mathcal{Z} imes \mathcal{Z}^*
ightarrow \Real$, which can be expanded to uncountable infinite domains such as continuous domains as well as interpolation. Thirdly, DualVAE fastly converges without sophisticated automatic/manual hyperparameter search in comparison to GANs as it requires only one additional parameter to VAE. Through the numerical experiment, we demonstrate the three benefits with multi-domain image generation task on CelebA with up to 60 domains, and exhibits that DualVAE records the state-of-the-art performance outperforming StarGAN and UFDN.
[EX A]: This paper proposes variational domain adaptation, a unified, scalable, simple framework for learning multiple distributions through variational inference

[EX Q]: Variational Autoencoders (VAEs) have proven to be powerful latent variable models. How- ever, the form of the approximate posterior can limit the expressiveness of the model. Categorical distributions are flexible and useful building blocks for example in neural memory layers. We introduce the Hierarchical Discrete Variational Autoencoder (HD-VAE): a hi- erarchy of variational memory layers. The Concrete/Gumbel-Softmax relaxation allows maximizing a surrogate of the Evidence Lower Bound by stochastic gradient ascent. We show that, when using a limited number of latent variables, HD-VAE outperforms the Gaussian baseline on modelling multiple binary image datasets. Training very deep HD-VAE remains a challenge due to the relaxation bias that is induced by the use of a surrogate objective. We introduce a formal definition and conduct a preliminary theoretical and empirical study of the bias.
[EX A]:
In this paper, we introduce a discrete hierarchy of categorical latent variables that we train using the Concrete/Gumbel-Softmax relaxation and we derive an upper bound for the absolute difference between the unbiased and the biased objective.