Visualizing the Loss Landscape of Neural Nets.¶
Li, H., Xu, Z., Taylor, G., Studer, C., & Goldstein, T. (2018). Visualizing the Loss Landscape of Neural Nets. Advances in Neural Information Processing Systems.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Optimization Landscape
- The intervention is structural rather than algorithmic: add residual connections that smooth the landscape, apply layer normalisation that reshapes the curvature, or change the initialisation to place the model inside a basin — any of which changes the topology to one where the existing algorithm works.
This source2D slices show how skip connections and normalization smooth neural loss surfaces.
- The intervention is structural rather than algorithmic: add residual connections that smooth the landscape, apply layer normalisation that reshapes the curvature, or change the initialisation to place the model inside a basin — any of which changes the topology to one where the existing algorithm works.
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Registry ID ref:d3fd614d5c1d · see in the full table