regularization strength
Amortized Inference Regularization
Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon
Canonically, the variational principle suggests to prefer an expressive inference model so that the variational approximation is accurate.
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Uncertainty-based Continual Learning with Adaptive Regularization
Hongjoon Ahn, Sungmin Cha, Donggyu Lee, Taesup Moon
Neural Information Processing Systems http://nips.cc/
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Cardinality-Regularized Hawkes-GrangerModel
We leverage the proposed algorithm for the task of instance-wise causal event analysis, where sparsity plays a critical role.
Amortized Inference Regularization
Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon
Canonically, the variational principle suggests to prefer an expressive inference model so that the variational approximation is accurate.
Country:
- North America > United States > California > Santa Clara County > Palo Alto (0.05)
- North America > Canada > Quebec > Montreal (0.04)
Technology:
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.47)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.30)
0d288dbe7a757672151aa5fb5423ef2e-Paper-Conference.pdf
Exp. Psychology, Oxford Brain Mind Institute, EPFL Jan P .
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