Scan Registration for Autonomous Mining Vehicles Using 3D-NDTKitsukawa Yuki
研究室のゼミの論文紹介の発表資料です。
Magnusson, M., Lilienthal, A. and Duckett, T. (2007), Scan registration for autonomous mining vehicles using 3D-NDT. J. Field Robotics, 24: 803–827. doi: 10.1002/rob.20204
Scan Registration for Autonomous Mining Vehicles Using 3D-NDTKitsukawa Yuki
研究室のゼミの論文紹介の発表資料です。
Magnusson, M., Lilienthal, A. and Duckett, T. (2007), Scan registration for autonomous mining vehicles using 3D-NDT. J. Field Robotics, 24: 803–827. doi: 10.1002/rob.20204
[DL Hacks] Deterministic Variational Inference for RobustBayesian Neural Networks (ICLR2019)
1. DEEP LEARNING JP
[DL Papers]
http://deeplearning.jp/
Deterministic Variational Inference for Robust
Bayesian Neural Networks (ICLR2019)
Makoto Kawano, Matsuo Lab.
2. 書誌情報
• 著者
Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner,
Jose Miguel Hernandez-Lobato, Alexander L. Gaunt
MSR Cambridge でのインターンの成果 (プリンストン大学の学生)
• ICLR2019 oral presentation: 7, 7, 7
• 選定理由:ICLR & ベイズ勉強のため
• TL;DR:ベイズニューラルネットワークの学習を安定化・高速化
した
•
2
26. 参考文献 I
Glorot, X. and Bengio, Y. (2010).
Understanding the difficulty of training deep feedforward neural networks.
In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pages
249–256.
Graves, A. (2011).
Practical variational inference for neural networks.
In Advances in neural information processing systems, pages 2348–2356.
He, K., Zhang, X., Ren, S., and Sun, J. (2015).
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.
In Proceedings of the IEEE international conference on computer vision, pages 1026–1034.
Hernández-Lobato, J. M. and Adams, R. (2015).
Probabilistic backpropagation for scalable learning of bayesian neural networks.
In International Conference on Machine Learning, pages 1861–1869.
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27. 参考文献 II
MacKay, D. J. (1992).
A practical bayesian framework for backpropagation networks.
Neural computation, 4(3):448–472.
Miller, A., Foti, N., D’Amour, A., and Adams, R. P. (2017).
Reducing reparameterization gradient variance.
In Advances in Neural Information Processing Systems, pages 3708–3718.
Tran, D., Mike, D., van der Wilk, M., and Hafner, D. (2018).
Bayesian layers: A module for neural network uncertainty.
arXiv preprint arXiv:1812.03973.
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