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20202026
most citedConvergence 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

2 citations · 4 across the 6 of their papers we have counts for

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2 papers · 1 filter

math.OC2022★ 2 cited

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…

math.OC2022★ 1 cited

On the existence of infinitely many realization functions of non-global local minima in the training of artificial neural networks with ReLU activation

Shokhrukh Ibragimov, Arnulf Jentzen, Timo Kröger +1

Gradient descent (GD) type optimization schemes are the standard instruments to train fully connected feedforward artificial neural networks (ANNs) with rectified linear unit (ReLU…