First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors present a novel non-parametric Bayesian model for unsupervised clustering. The model uses a two level hierarchy of Dirichlet process priors to handle clusters which may be multi-modal, skewed and/or heavy tailed. The authors present a collapsed Gibbs sampler for inference which exploits the conjugacy of the model. The authors do an excellent job of motivating the model by explaining the deficiencies of the standard infinite mixture of Gaussians.

Dirichlet process mixture of Gaussians (DPMG) has been used in the literature for clustering and density estimation problems. However, many real-world data exhibit cluster distributions that cannot be captured by a single Gaussian. Modeling such data sets by DPMG creates several extraneous clusters even when clusters are relatively well-defined.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. As a major novelty, the authors propose that the stochasticity of synaptic transmission is directly involved in the implementation of stochasticity necessary for Monte Carlo sampling. The neurons used throughout the paper are binary threshold units and not spiking neurons. These binary neurons are able to provide useful insights into how a neuronal network may solve computational problems, but it is important to distinguish between implementations using binary units and spiking neurons. The authors include a short section about spike-based implementation in the appendix, but they do not demonstrate that the spike based implementation is able to perform the same tasks with similar performance.

We propose a spiking network model capable of performing both approximate inference and learning for any hidden Markov model. The lower layer sensory neurons detect noisy measurements of hidden world states. The higher layer neurons with recurrent connections infer a posterior distribution over world states from spike trains generated by sensory neurons. We show how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in the population of inference neurons represents a sample of a particular hidden world state.

The profile of a sample is the multiset of its symbol frequencies. We show that for samples of discrete distributions, profile entropy is a fundamental measure unifying the concepts of estimation, inference, and compression.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. In this paper, authors analyze sparsity of the posterior parameters in LDA using a variational Bayesian algorithm. They derive an expression for the VB free energy which shows its asymptotic behaviour with respect to number of words (N), number of documents (M), vocabulary size (L) etc. Their results suggest that, for certain settings of L,M,N, the sparsity behaviour changes drastically at a particular hyper-parameter setting. These changes differ from those of MAP and partial-Bayes algorithms. The problem discussed in this paper is original, interesting, and is perhaps useful too.

Generated samples on CelebA-HQ 256 256 (left) and unconditional CIFAR10 (right) 34th Conference on Neural Information Processing Systems (NeurIPS 2020), V ancouver, Canada.