26 citations · 96 across the 22 of their papers we have counts for
3 papers · 1 filter
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…
Normalized gradient flow optimization in the training of ReLU artificial neural networks
Simon Eberle, Arnulf Jentzen, Adrian Riekert +1
The training of artificial neural networks (ANNs) is nowadays a highly relevant algorithmic procedure with many applications in science and industry. Roughly speaking, ANNs can be…
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…