Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 4: Ok but not good enough - rejection
review_content: The particular topic of high energy physics would likely be of marginal interest to most attendees, though I might be wrong about this. What's more the paper does not present any results improving on current state of the art based on analytical techniques. 

Since the authors' stated goal is to seek advice and input from the learning community at large, it would seem that they would be better served by attending ICLR and striking up conversations with researchers with relevant experience rather than organising a topical workshop.
review_title: marginal topic, partial results

final_score: rating: 4: Ok but not good enough - rejection
review_content: * The authors seem to acknowledge the premature nature of this submission in the last sentence of discussion, and focus on seeking advice. I scored the paper in standard way. 

The paper evaluates LSTM vs kalman filter on particle tracking problem in high-energy physics, using simulated data. The paper presents a well-motivated problem, but provides no significant results/novel insights.

Pros: 
-Well-motivated problem
-Part of interesting open project: https://heptrkx.github.io/

Cons:
-Experimental results are not surprising nor conclusive. Imperfect training, insufficient network capacity or expressivity, overfitting, limited data, etc. can easily cause LSTM to underperform best kalman filter with expert knowledge. Experimental descriptions do not show sufficient depths in evaluation.
-The motivated problem/solution is not novel. There are a number of prior work that can be cited for nonlinear state estimation with neural network or combining prior with neural net. Structured VAE (Johnson et. al., 2016), deep KF (Krishnan et. al., 2015), for example, explored incorporating structured prior with rich neural network parametrized observation model. Backprop KF (Haarnoja et. al., 2016) combined discriminative training into state estimation and avoided some problems of these generative model papers.  
-Run-time of different implementations should be detailed with scalability analysis, as that is one of the main motivations.

review_title: Preliminary work

Decision: Reject