2 papers
cs.LG2021
Neurons learn slower than they think
Ilona Kulikovskikh
Recent studies revealed complex convergence dynamics in gradient-based methods, which has been little understood so far. Changing the step size to balance between high convergence…
cs.LG2020
Why to "grow" and "harvest" deep learning models?
Ilona Kulikovskikh, Tarzan Legović
Current expectations from training deep learning models with gradient-based methods include: 1) transparency; 2) high convergence rates; 3) high inductive biases. While the state-o…