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The posts in this blog are written by @hyemi, @uiwon and @jisoo
[1.Variational Inference]({{ "/" | relative_url }}/background/2020/04/09/vi/)
- Keeping the neural networks simple by minimizing the description length of the weights. [Hinton, G. E., & Van Camp, D. 1993]
- Ensemble learning in Bayesian neural networks. [Barber, D., & Bishop, C. M. 1998]
- Practical variational inference for neural networks. [Graves, A. 2011]
- Weight uncertainty in neural networks [Blundell, Charles, et al. 2015]
- Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. [Gal, Y., & Ghahramani, Z. 2015]
- Stochastic gradient descent as approximate Bayesian inference. [Mandt, S., Hoffman, M. D., & Blei, D. M. 2017]
- Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam [Khan, Mohammad Emtiyaz, et al. 2018]