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.

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

Understanding the optimization trajectory is critical to understand training of deep neural networks. We show how the hyperparameters of stochastic gradient descent influence the covariance of the gradients (K) and the Hessian of the training loss (H) along this trajectory. Based on a theoretical model, we predict that using a high learning rate or a small batch size in the early phase of training leads SGD to regions of the parameter space with (1) reduced spectral norm of K, and (2) improved conditioning of K and H. We show that the point on the trajectory after which these effects hold, which we refer to as the break-even point, is reached early during training. We demonstrate these effects empirically for a range of deep neural networks applied to multiple different tasks. Finally, we apply our analysis to networks with batch normalization (BN) layers and find that it is necessary to use a high learning rate to achieve loss smoothing effects attributed previously to BN alone.
In the early phase of training of deep neural networks there exists a "break-even point" which determines properties of the entire optimization trajectory.

Recent empirical results on over-parameterized deep networks are marked by a striking absence of the classic U-shaped test error curve: test error keeps decreasing in wider networks. Researchers are actively working on bridging this discrepancy by proposing better complexity measures. Instead, we directly measure prediction bias and variance for four classification and regression tasks on modern deep networks. We find that both bias and variance can decrease as the number of parameters grows. Qualitatively, the phenomenon persists over a number of gradient-based optimizers. To better understand the role of optimization, we decompose the total variance into variance due to training set sampling and variance due to initialization. Variance due to initialization is significant in the under-parameterized regime. In the over-parameterized regime, total variance is much lower and dominated by variance due to sampling. We provide theoretical analysis in a simplified setting that is consistent with our empirical findings.

We provide evidence against classical claims about the bias-variance tradeoff and propose a novel decomposition for variance.