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.

Input: Consider Input: 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.


Output: Improve the robustness and energy efficiency of a deep neural network using the hidden representations.


Input: Consider Input: We describe two end-to-end autoencoding models for semi-supervised graph-based dependency parsing. The first model is a Local Autoencoding Parser (LAP) encoding the input using continuous latent variables in a sequential manner; The second model is a Global Autoencoding Parser (GAP) encoding the input into dependency trees as latent variables, with exact inference. Both models consist of two parts: an encoder enhanced by deep neural networks (DNN) that can utilize the contextual information to encode the input into latent variables, and a decoder which is a generative model able to reconstruct the input. Both LAP and GAP admit a unified structure with different loss functions for labeled and unlabeled data with shared parameters. We conducted experiments on WSJ and UD dependency parsing data sets, showing that our models can exploit the unlabeled data to boost the performance given a limited amount of labeled data.

Output: We describe two end-to-end autoencoding parsers for semi-supervised graph-based dependency parsing.


Input: Consider Input: We study the problem of alleviating the instability issue in the GAN training procedure via new architecture design. The discrepancy between the minimax and maximin objective values could serve as a proxy for the difficulties that the alternating gradient descent encounters in the optimization of GANs. In this work, we give new results on the benefits of multi-generator architecture of GANs. We show that the minimax gap shrinks to \epsilon as the number of generators increases with rate O(1/\epsilon). This improves over the best-known result of O(1/\epsilon^2). At the core of our techniques is a novel application of Shapley-Folkman lemma to the generic minimax problem, where in the literature the technique was only known to work when the objective function is restricted to the Lagrangian function of a constraint optimization problem. Our proposed Stackelberg GAN performs well experimentally in both synthetic and real-world datasets, improving Frechet Inception Distance by 14.61% over the previous multi-generator GANs on the benchmark datasets.


Output: We study the problem of alleviating the instability issue in the GAN training procedure via new architecture design, with theoretical guarantees.