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1.[1609.05191] Gradient Descent Learns Linear Dynamical Systems
- Author: [1609.05191]
- Publish: 27 days ago
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- Source : https://arxiv.org/abs/1609.05191
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2.[PDF] Gradient Descent Learns Linear Dynamical Systems
- Author: [PDF]
- Publish: 26 days ago
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Rating: 3(955 Rating)
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- Descriptions: Maximum likelihood estimation with gradient descent is a popular heuristic for dynamical system identification Ljung (1998). In the context of machine learn-.
- More : Maximum likelihood estimation with gradient descent is a popular heuristic for dynamical system identification Ljung (1998). In the context of machine learn-.
- Source : https://jmlr.org/papers/volume19/16-465/16-465.pdf
3.Gradient Descent Learns Linear Dynamical Systems
- Author: Gradient
- Publish: 28 days ago
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Rating: 2(1506 Rating)
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- Descriptions: We prove that stochastic gradient descent efficiently converges to the global optimizer of the maximum likelihood objective of an unknown linear time-invariant …
- More : We prove that stochastic gradient descent efficiently converges to the global optimizer of the maximum likelihood objective of an unknown linear time-invariant …
- Source : https://jmlr.org/papers/v19/16-465.html
4.[PDF] Gradient Descent Learns Linear Dynamical Systems
- Author: [PDF]
- Publish: 19 days ago
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Rating: 4(202 Rating)
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Highest rating: 4
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Lowest rating: 1
- Descriptions: Maximum likelihood estimation with gradient descent is a popular heuristic for dynamical system identification Ljung (1998). In the context of machine learn-.
- More : Maximum likelihood estimation with gradient descent is a popular heuristic for dynamical system identification Ljung (1998). In the context of machine learn-.
- Source : https://jmlr.csail.mit.edu/papers/volume19/16-465/16-465.pdf
5.Gradient Descent Learns Linear Dynamical Systems
- Author: Gradient
- Publish: 20 days ago
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Rating: 2(1746 Rating)
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- Source : http://www.offconvex.org/2016/10/13/gradient-descent-learns-dynamical-systems/
6.Gradient Descent Learns Linear Dynamical Systems – NASA/ADS
- Author: Gradient
- Publish: 14 days ago
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Rating: 4(511 Rating)
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- Descriptions: … gradient descent efficiently converges to the global optimizer of the maximum likelihood objective of an unknown linear time-invariant dynamical system …
- More : … gradient descent efficiently converges to the global optimizer of the maximum likelihood objective of an unknown linear time-invariant dynamical system …
- Source : https://ui.adsabs.harvard.edu/abs/2016arXiv160905191H/abstract
7.Gradient Descent Learns Linear Dynamical Systems – ResearchGate
- Author: Gradient
- Publish: 21 days ago
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Rating: 3(1009 Rating)
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Highest rating: 3
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- Descriptions: We prove that gradient descent efficiently converges to the global optimizer of the maximum likelihood objective of an unknown linear time-invariant …
- More : We prove that gradient descent efficiently converges to the global optimizer of the maximum likelihood objective of an unknown linear time-invariant …
- Source : https://www.researchgate.net/publication/308262770_Gradient_Descent_Learns_Linear_Dynamical_Systems
8.Gradient descent learns linear dynamical systems
- Author: Gradient
- Publish: 28 days ago
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Rating: 3(552 Rating)
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- Source : https://dl.acm.org/doi/10.5555/3291125.3291154
9.[PDF] Gradient Descent Learns Linear Dynamical Systems
- Author: [PDF]
- Publish: 7 days ago
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Rating: 1(1496 Rating)
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- Source : https://www.semanticscholar.org/paper/Gradient-Descent-Learns-Linear-Dynamical-Systems-Hardt-Ma/8a765725a44b91b60d414551dc555175cfff3cd9
10.Online Gradient Descent for Linear Dynamical Systems
- Author: Online
- Publish: 19 days ago
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Rating: 1(856 Rating)
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Highest rating: 4
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Lowest rating: 2
- Descriptions: Abbasi-Yadkori, Y., Bartlett, P., and Kanade, V. (2014). Tracking adversarial targets. In Proceedings of the 31st International Conference on Machine Learning, …
- More : Abbasi-Yadkori, Y., Bartlett, P., and Kanade, V. (2014). Tracking adversarial targets. In Proceedings of the 31st International Conference on Machine Learning, …
- Source : https://www.sciencedirect.com/science/article/pii/S2405896320316608
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