6 citations · 11 across the 5 of their papers we have counts for
5 papers
Automatic Gradient Descent: Deep Learning without Hyperparameters
Jeremy Bernstein, Chris Mingard, Kevin Huang +2
The architecture of a deep neural network is defined explicitly in terms of the number of layers, the width of each layer and the general network topology. Existing optimisation fr…
Beyond Monotone Variational Inequalities: Solution Methods and Iteration Complexities
Kevin Huang, Shuzhong Zhang
In this paper, we discuss variational inequality (VI) problems without monotonicity from the perspective of convergence of projection-type algorithms. In particular, we identify ex…
Thrust Enhancement and Degradation Mechanisms due to Self-Induced Vibrations in Bio-inspired Flying Robots
Dipan Deb, Kevin Huang, Aakash Verma +2
Whenever a flapping robot moves along a trajectory it experiences some vibration about its mean path. Even for a hovering case, a flier experiences such vibration due to the oscill…
An Approximation-Based Regularized Extra-Gradient Method for Monotone Variational Inequalities
Kevin Huang, Shuzhong Zhang
In this paper, we propose a general extra-gradient scheme for solving monotone variational inequalities (VI), referred to here as Approximation-based Regularized Extra-gradient met…
Cubic Regularized Newton Method for Saddle Point Models: a Global and Local Convergence Analysis
Kevin Huang, Junyu Zhang, Shuzhong Zhang
In this paper, we propose a cubic regularized Newton (CRN) method for solving convex-concave saddle point problems (SPP). At each iteration, a cubic regularized saddle point subpro…