Paper:
Bayesian inference promises to ground and improve the performance of deep neural networks. It promises to be robust to overfitting, to simplify the training procedure and the space of hyperparameters, and to provide a calibrated measure of uncertainty that can enhance decision making, agent exploration and prediction fairness. Markov Chain Monte Carlo (MCMC) methods enable Bayesian inference by generating samples from the posterior distribution over model parameters. Despite the theoretical advantages of Bayesian inference and the similarity between MCMC and optimization methods, the performance of sampling methods has so far lagged behind optimization methods for large scale deep learning tasks. We aim to fill this gap and introduce ATMC, an adaptive noise MCMC algorithm that estimates and is able to sample from the posterior of a neural network. ATMC dynamically adjusts the amount of momentum and noise applied to each parameter update in order to compensate for the use of stochastic gradients. We use a ResNet architecture without batch normalization to test ATMC on the Cifar10 benchmark and the large scale ImageNet benchmark and show that, despite the absence of batch normalization, ATMC outperforms a strong optimization baseline in terms of both classification accuracy and test log-likelihood. We show that ATMC is intrinsically robust to overfitting on the training data and that ATMC provides a better calibrated measure of uncertainty compared to the optimization baseline.

Review:
Summary:
The main idea of the paper is the introduction of ATMC, an adaptive noise MCMC algorithm that dynamically adjusts the momentum and noise applied to each parameter update while sampling from the posterior of a neural network. A modified version of ResNet architecture called ResNet++ has been introduced in the paper and later used to train it on ImageNet using MCMC which is great work considering the complexities associated. Furthermore, the authors claim ATMC to be robust to overfitting and it provides a measure of uncertainty with predictions. The paper is well written with clear flow and good mathematical explanation. 
Questions from authors:
1. The approach of Stochastic Gradient MCMC (SG-MCMC) has been there for quite some time and in this paper, there is no clear explanation of the advantages of ATMC over SG-MCMC methods. The authors can argue that the introduction of dynamically adjustable momentum and noise can make the paper novel and unique. However, in Stochastic gradient Markov chain Monte Carlo (SG-MCMC) [1], the authors used a meta-learning algorithm to learn Hamiltonian dynamics with state-dependent drift and diffusion which makes the system scalable to large datasets, very similar to this work. Also, changing the momentum variable in Hamiltonian dynamics with thermostat variable in stochastic gradient thermostats, the meta learner algorithm of [1] can be used to make the stochastic gradient thermostats approach adaptive. What are the advantages of the approach proposed in this paper over [1]?
2.  The paper follows the approach of stochastic gradient thermostats as introduced in the paper  [2] and builds upon it to make it adaptive to improve stability and for better convergence.  The advantages are clearly mentioned in the paper except for the case when 0 < ξ < D_c the total amount of noise added to the momentum is D_c and the friction coefficient β(ξ) = D_c. At this stage, the authors claim that 'the stochastic gradient noise is compensated for by adding less noise to the momentum update.' How is this done? Can authors please explain it a bit more.
3.  For the experiments, the authors introduced ResNet++ architecture which is based on ResNet architecture but is novel in its design with the use of SELU and removal of BatchNorm and with different initialization schemes. The design idea is well explained by the authors. The experiments were performed on CIFAR10 and ImageNet dataset to show the large scale scaling of the approach. A comparison with SGNHT is provided in the paper but a fair comparison with other approaches like SGHMC [3], PSGLD [4] and MCMC [1] is missing which might have proved the effectiveness of the approach when compared to other methods. Any comments on why the experiments were restricted to the ones mentioned in the paper?
4. Lastly, the ResNet++ network is trained on ImageNet and CIFAR10 datasets. There is no clear mention of the time duration it took for training and evaluating the network. The authors mentioned ‘BatchNorm did result in an overhead of roughly 20% compared to the ResNet++ model’, which is not clear. I would urge the authors to explain the above line and provide an estimate of the training time and testing time. 
A closing remark: The mention of the acknowledgement section in a double-blind review is not advisable and in future please refrain from doing so.
References:
[1] https://openreview.net/pdf?id=HkeoOo09YX#page=11&zoom=100,0,754
[2] http://people.ee.duke.edu/~lcarin/sgnht-4.pdf
[3] https://arxiv.org/pdf/1402.4102.pdf
[4] https://arxiv.org/abs/1512.07666