2 citations · 4 across the 6 of their papers we have counts for
4 papers · 1 filter
Landscape analysis for shallow neural networks: Complete classification of critical points for cubic activation and affine target functions
Shokhrukh Ibragimov, Ilkhom Mukhammadiev, Diyora Salimova
In this paper, we study the optimization landscape induced by the true loss for shallow polynomial neural networks (PNNs) with neurons on the hidden l…
Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks
Shokhrukh Ibragimov, Arnulf Jentzen
Gradient based optimization methods are nowadays the methods of choice for training deep neural networks (DNNs) in artificial intelligence (AI) systems. In practically relevant DNN…
Convergence to good non-optimal critical points in the training of neural networks: Gradient descent optimization with one random initialization overcomes all bad non-global local minima with high probability
Shokhrukh Ibragimov, Arnulf Jentzen, Adrian Riekert
Gradient descent (GD) methods for the training of artificial neural networks (ANNs) belong nowadays to the most heavily employed computational schemes in the digital world. Despite…
On the existence of infinitely many realization functions of non-global local minima for artificial neural networks with ReLU activation
Shokhrukh Ibragimov, Arnulf Jentzen, Timo Kröger +1
Gradient descent (GD) type optimization schemes are standard tools for training fully connected feedforward artificial neural networks (ANNs) with rectified linear unit (ReLU) acti…