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: Kronecker-factor Approximate Curvature (Martens & Grosse, 2015) (K-FAC) is a 2nd-order optimization method which has been shown to give state-of-the-art performance on large-scale neural network optimization tasks (Ba et al., 2017).   It is based on an approximation to the Fisher information matrix (FIM) that makes assumptions about the particular structure of the network and the way it is parameterized. The original K-FAC method was applicable only to fully-connected networks, although it has been recently extended by Grosse & Martens (2016) to handle convolutional networks as well. In this work we extend the method to handle RNNs by introducing a novel approximation to the FIM for RNNs. This approximation works by modelling the covariance structure between the gradient contributions at different time-steps using a chain-structured linear Gaussian graphical model, summing the various cross-covariances, and computing the inverse in closed form. We demonstrate in experiments that our method significantly outperforms general purpose state-of-the-art optimizers like SGD with momentum and Adam on several challenging RNN training tasks.

Output: We extend the K-FAC method to RNNs by developing a new family of Fisher approximations.


Input: Consider Input: We introduce a novel workflow, QCue, for providing textual stimulation during mind-mapping. Mind-mapping is a powerful tool whose intent is to allow one to externalize ideas and their relationships surrounding a central problem. The key challenge in mind-mapping is the difficulty in balancing the exploration of different aspects of the problem (breadth) with a detailed exploration of each of those aspects (depth). Our idea behind QCue is based on two mechanisms: (1) computer-generated automatic cues to stimulate the user to explore the breadth of topics based on the temporal and topological evolution of a mind-map and (2) user-elicited queries for helping the user explore the depth for a given topic. We present a two-phase study wherein the first phase provided insights that led to the development of our work-flow for stimulating the user through cues and queries. In the second phase, we present a between-subjects evaluation comparing QCue with a digital mind-mapping work-flow without computer intervention. Finally, we present an expert rater evaluation of the mind-maps created by users in conjunction with user feedback.

Output: This paper introduces a method to generate questions (cues) and queries (suggestions) to help users perform mind-mapping.


Input: Consider Input: Unsupervised monocular depth estimation has made great progress after deep
 learning is involved. Training with binocular stereo images is considered as a
 good option as the data can be easily obtained. However, the depth or disparity
 prediction results show poor performance for the object boundaries. The main
 reason is related to the handling of occlusion areas during the training. In this paper,
 we propose a novel method to overcome this issue. Exploiting disparity maps
 property, we generate an occlusion mask to block the back-propagation of the occlusion
 areas during image warping. We also design new networks with flipped
 stereo images to induce the networks to learn occluded boundaries. It shows that
 our method achieves clearer boundaries and better evaluation results on KITTI
 driving dataset and Virtual KITTI dataset.


Output: This paper propose a mask method which solves the previous blurred results of unsupervised monocular depth estimation caused by occlusion