Gradient Descent
Regularized Gradient Descent Ascentfor Two-Player Zero-Sum Markov Games
Under >0, ( )is 2 SB, and ( )isunique 2 SA.
Deep Leakage from Gradients
Ligeng Zhu, Zhijian Liu, Song Han
Neural Information Processing Systems http://nips.cc/
Learning to Learn By Self-Critique
Antreas Antoniou, Amos J. Storkey
Neural Information Processing Systems http://nips.cc/
Gradient Descent for Spiking Neural Networks
Dongsung Huh, Terrence J. Sejnowski
Neural Information Processing Systems http://nips.cc/
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies
Basri Ronen, David Jacobs, Yoni Kasten, Shira Kritchman
We study the relationship between the frequency of a function and the speed at which a neural network learns it. We build on recent results that show that the dynamics of overparameterized neural networks trained with gradient descent can bewell approximated byalinear system. When normalized training data is uniformly distributed on ahypersphere, the eigenfunctions of this linear system are spherical harmonic functions.
On Markov Chain Gradient Descent
Tao Sun, Yuejiao Sun, Wotao Yin
Neural Information Processing Systems http://nips.cc/
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes
Loucas Pillaud-Vivien, Alessandro Rudi, Francis Bach
Neural Information Processing Systems http://nips.cc/