Visualizing the Loss Landscape of Winning Lottery Tickets
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Abstract (arXiv)
The underlying loss landscapes of deep neural networks have a great impact on their training, but they have mainly been studied theoretically due to computational constraints. This work vastly reduces the time required to compute such loss landscapes, and uses them to study winning lottery tickets found via iterative magnitude pruning. We also share results that contradict previously claimed correlations between certain loss landscape projection methods and model trainability and generalization error.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 15 references that are in the map. First posted on arXiv 16 Dec 2021.
Builds on
13 of 15 references.
References · 13
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- 2021Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networkscited by 14
1 more
- 2021LossPlot: A Better Way to Visualize Loss Landscapescited by 5
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Citing papers per year: 2022: 2.
2 papers.
- 2022Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspectivecited by 9
- 2022The Role Of Biology In Deep Learningcited by 0
- 2022The Role Of Biology In Deep Learning7 Sep 2022
- 2022Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective15 May 2022