26 citations · 30 across the 2 of their papers we have counts for
3 papers
stat.ML2019
Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems
Atsushi Nitanda, Geoffrey Chinot, Taiji Suzuki
Recently, several studies have proven the global convergence and generalization abilities of the gradient descent method for two-layer ReLU networks. Most studies especially focuse…
stat.ML2017★ 26 cited
Stochastic Particle Gradient Descent for Infinite Ensembles
Atsushi Nitanda, Taiji Suzuki
The superior performance of ensemble methods with infinite models are well known. Most of these methods are based on optimization problems in infinite-dimensional spaces with some…
stat.ML2015★ 4 cited
Accelerated Stochastic Gradient Descent for Minimizing Finite Sums
Atsushi Nitanda
We propose an optimization method for minimizing the finite sums of smooth convex functions. Our method incorporates an accelerated gradient descent (AGD) and a stochastic variance…