Towards Unsupervised Classification with Deep Generative Models
Deep generative models have advanced the state-of-the-art in semi-supervised classification, however their capacity for deriving useful discriminative features in a completely unsupervised fashion for classification in difficult real-world data sets, where adequate manifold separation is required has not been adequately explored. Most methods rely on defining a pipeline of deriving features via generative modeling and then applying clustering algorithms, separating the modeling and discriminative processes. We propose a deep hierarchical generative model which uses a mixture of discrete and continuous distributions to learn to effectively separate the different data manifolds and is trainable end-to-end. We show that by specifying the form of the discrete variable distribution we are imposing a specific structure on the model*s latent representations. We test our model*s discriminative performance on the task of CLL diagnosis against baselines from the field of computational FC, as well as the Variational Autoencoder literature.
 The authors propose a deep hierarchical model for unsupervised classification by using a combination of latent continuous and discrete distributions. Although, the detailed description of flow cytometry and chronic lymphocytic leukemia are appreciated, they are probably out of the scope of the paper or not relevant for the presented approach. The authors claim that existing approaches for clustering cell populations in flow cytometry data are sensitive to noise and rely on cumbersome hyperparameter specifications, which in some sense is true, however, that does not mean that the proposed approach is less sensitive to noise or that that the proposed model has less free-parameters to tune (layers, hidden units, regularization, step size, link function, etc.). In fact, it is not clear how the authors would be able to define a model architecture without label information, what would be the model selection metric to optimize, ELBO?. At least this very issue is not addressed in the manuscript. In Figure 1, please use different colors for different cell types. It is not described, but it would be good to stress out that each of the 4 components in Figure 1 right, corresponds to a mixture component. The results in Tables 1 and 2 are not very convincing without clarity on the selection of the thresholds for each of the models. It would be better to report threshold-free metrics such as area under the ROC or PR curve. From Figures 3 and 4 for example, it is difficult to grasp the performance gap between the proposed approach and .beta-VAE. - FC and CLL are not spelled out in the introduction. - Equation (5) is confusing, what is h, y = h or is h a mixture of Gaussians with .alpha mixing proportions? - Equation (6) should be q(z_L|z) - Equation (8) is again confusing. - Equation (10) is not correct, x can*t be conditioned on h, as it is clearly conditioned on z_1. - Equation (11) it should be q_.phi(). - It is not clear why the probabilities are thresholded at 0.5 - Figures 3 and 4 could use larger markers and font sizes.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Towards Unsupervised Classification with Deep Generative Models
Deep generative models have advanced the state-of-the-art in semi-supervised classification, however their capacity for deriving useful discriminative features in a completely unsupervised fashion for classification in difficult real-world data sets, where adequate manifold separation is required has not been adequately explored. Most methods rely on defining a pipeline of deriving features via generative modeling and then applying clustering algorithms, separating the modeling and discriminative processes. We propose a deep hierarchical generative model which uses a mixture of discrete and continuous distributions to learn to effectively separate the different data manifolds and is trainable end-to-end. We show that by specifying the form of the discrete variable distribution we are imposing a specific structure on the model*s latent representations. We test our model*s discriminative performance on the task of CLL diagnosis against baselines from the field of computational FC, as well as the Variational Autoencoder literature.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The authors propose a deep hierarchical model for unsupervised classification by using a combination of latent continuous and discrete distributions. Although, the detailed description of flow cytometry and chronic lymphocytic leukemia are appreciated, they are probably out of the scope of the paper or not relevant for the presented approach. The authors claim that existing approaches for clustering cell populations in flow cytometry data are sensitive to noise and rely on cumbersome hyperparameter specifications, which in some sense is true, however, that does not mean that the proposed approach is less sensitive to noise or that that the proposed model has less free-parameters to tune (layers, hidden units, regularization, step size, link function, etc.). In fact, it is not clear how the authors would be able to define a model architecture without label information, what would be the model selection metric to optimize, ELBO?. At least this very issue is not addressed in the manuscript. In Figure 1, please use different colors for different cell types. It is not described, but it would be good to stress out that each of the 4 components in Figure 1 right, corresponds to a mixture component. The results in Tables 1 and 2 are not very convincing without clarity on the selection of the thresholds for each of the models. It would be better to report threshold-free metrics such as area under the ROC or PR curve. From Figures 3 and 4 for example, it is difficult to grasp the performance gap between the proposed approach and .beta-VAE. - FC and CLL are not spelled out in the introduction. - Equation (5) is confusing, what is h, y = h or is h a mixture of Gaussians with .alpha mixing proportions? - Equation (6) should be q(z_L|z) - Equation (8) is again confusing. - Equation (10) is not correct, x can*t be conditioned on h, as it is clearly conditioned on z_1. - Equation (11) it should be q_.phi(). - It is not clear why the probabilities are thresholded at 0.5 - Figures 3 and 4 could use larger markers and font sizes.
assistant
 The authors propose a deep hierarchical model for unsupervised classification by using a combination of latent continuous and discrete distributions. Although, the detailed description of flow cytometry and chronic lymphocytic leukemia are appreciated, they are probably out of the scope of the paper or not relevant for the presented approach. The authors claim that existing approaches for clustering cell populations in flow cytometry data are sensitive to noise and rely on cumbersome hyperparameter specifications, which in some sense is true, however, that does not mean that the proposed approach is less sensitive to noise or that that the proposed model has less free-parameters to tune (layers, hidden units, regularization, step size, link function, etc.). In fact, it is not clear how the authors would be able to define a model architecture without label information, what would be the model selection metric to optimize, ELBO?. At least this very issue is not addressed in the manuscript. In Figure 1, please use different colors for different cell types. It is not described, but it would be good to stress out that each of the 4 components in Figure 1 right, corresponds to a mixture component. The results in Tables 1 and 2 are not very convincing without clarity on the selection of the thresholds for each of the models. It would be better to report threshold-free metrics such as area under the ROC or PR curve. From Figures 3 and 4 for example, it is difficult to grasp the performance gap between the proposed approach and .beta-VAE. - FC and CLL are not spelled out in the introduction. - Equation (5) is confusing, what is h, y = h or is h a mixture of Gaussians with .alpha mixing proportions? - Equation (6) should be q(z_L|z) - Equation (8) is again confusing. - Equation (10) is not correct, x can*t be conditioned on h, as it is clearly conditioned on z_1. - Equation (11) it should be q_.phi(). - It is not clear why the probabilities are thresholded at 0.5 - Figures 3 and 4 could use larger markers and font sizes.