Unified recurrent network for many feature types
There are time series that are amenable to recurrent neural network (RNN) solutions when treated as sequences, but some series, e.g. asynchronous time series, provide a richer variation of feature types than current RNN cells take into account. In order to address such situations, we introduce a unified RNN that handles five different feature types, each in a different manner. Our RNN framework separates sequential features into two groups dependent on their frequency, which we call sparse and dense features, and which affect cell updates differently. Further, we also incorporate time features at the sequential level that relate to the time between specified events in the sequence and are used to modify the cell*s memory state. We also include two types of static (whole sequence level) features, one related to time and one not, which are combined with the encoder output. The experiments show that the proposed modeling framework does increase performance compared to standard cells.
 The paper addresses limitations of the LTSM method for modeling time series. The work is motivated by applications where multiple time series need to be combined while they may get updated in an asynchronous fashion. Authors mention IoT applications in the Intro and give examples from a power consumption data set and a churn prediction application in their numerical experiments sections. The paper*s main contribution is to shrink down time series into 5 different categories which have different sampling schemes: (1) dense (2) sparse (3) decay (4) static decay (3) static standard, and proposing building blocks to incorporate these in a unified recurrent mechanism. The paper*s motivation for introducing sparse input to LTSM was rather straightforward and convincing, and the churn prediction application is an excellent motivation for this. I had a harder time following why the 3 other types of features needed to be included as well at this point. Perhaps a more problem oriented explanation with concrete examples could have helped. Or those features should not yet be used as generalizations but kept for future work. Overall I think the paper has several interesting ideas. Even though not all are as convincing, I think the paper is thought provoking and may interest the ICLR community.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Unified recurrent network for many feature types
There are time series that are amenable to recurrent neural network (RNN) solutions when treated as sequences, but some series, e.g. asynchronous time series, provide a richer variation of feature types than current RNN cells take into account. In order to address such situations, we introduce a unified RNN that handles five different feature types, each in a different manner. Our RNN framework separates sequential features into two groups dependent on their frequency, which we call sparse and dense features, and which affect cell updates differently. Further, we also incorporate time features at the sequential level that relate to the time between specified events in the sequence and are used to modify the cell*s memory state. We also include two types of static (whole sequence level) features, one related to time and one not, which are combined with the encoder output. The experiments show that the proposed modeling framework does increase performance compared to standard cells.
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 paper addresses limitations of the LTSM method for modeling time series. The work is motivated by applications where multiple time series need to be combined while they may get updated in an asynchronous fashion. Authors mention IoT applications in the Intro and give examples from a power consumption data set and a churn prediction application in their numerical experiments sections. The paper*s main contribution is to shrink down time series into 5 different categories which have different sampling schemes: (1) dense (2) sparse (3) decay (4) static decay (3) static standard, and proposing building blocks to incorporate these in a unified recurrent mechanism. The paper*s motivation for introducing sparse input to LTSM was rather straightforward and convincing, and the churn prediction application is an excellent motivation for this. I had a harder time following why the 3 other types of features needed to be included as well at this point. Perhaps a more problem oriented explanation with concrete examples could have helped. Or those features should not yet be used as generalizations but kept for future work. Overall I think the paper has several interesting ideas. Even though not all are as convincing, I think the paper is thought provoking and may interest the ICLR community.
assistant
 The paper addresses limitations of the LTSM method for modeling time series. The work is motivated by applications where multiple time series need to be combined while they may get updated in an asynchronous fashion. Authors mention IoT applications in the Intro and give examples from a power consumption data set and a churn prediction application in their numerical experiments sections. The paper*s main contribution is to shrink down time series into 5 different categories which have different sampling schemes: (1) dense (2) sparse (3) decay (4) static decay (3) static standard, and proposing building blocks to incorporate these in a unified recurrent mechanism. The paper*s motivation for introducing sparse input to LTSM was rather straightforward and convincing, and the churn prediction application is an excellent motivation for this. I had a harder time following why the 3 other types of features needed to be included as well at this point. Perhaps a more problem oriented explanation with concrete examples could have helped. Or those features should not yet be used as generalizations but kept for future work. Overall I think the paper has several interesting ideas. Even though not all are as convincing, I think the paper is thought provoking and may interest the ICLR community.