Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper.
Query: Negative eigenvalues of the Hessian in deep neural networks
Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond
Exact Calculation of the Hessian Matrix for the Multilayer Perceptron
Lipschitz regularity of deep neural networks : analysis and efficient estimation
Gradient Descent Finds Global Minima of Deep Neural Networks
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
Training Deep and Recurrent Networks with Hessian-Free Optimization
N ov 2 01 8 Gradient Descent Finds Global Minima of Deep Neural Networks
Meta-Learning with Hessian-Free Approach in Deep Neural Nets Training
Almost Global Convergence to Global Minima for Gradient Descent in Deep Linear Networks
Distributed Newton Methods for Deep Neural Networks
Local minima in training of neural networks
Avoiding pathologies in very deep networks
Trivializing The Energy Landscape Of Deep Networks.
Deriving optimal weights in deep neural networks
On the Depth of Deep Neural Networks: A Theoretical View
Distributed Hessian-Free Optimization for Deep Neural Network
Lipschitz Properties for Deep Convolutional Networks
On the Number of Linear Regions of Deep Neural Networks
Mathematics of Deep Learning
Understanding the difficulty of training deep feedforward neural networks
A ferrite permanent magnet axial gap motor with segmented rotor structure for the next generation hybrid vehicle
PatternNet and PatternLRP - Improving the interpretability of neural networks.
On the difficulty of training Recurrent Neural Networks
fraglets – a metabolistic execution model for communication protocols .
Memristor for computing: Myth or reality?