Venue: ICLR 2017 conference

Reviews:

final_score: rating: 2: Strong rejection
review_content: This paper proposes a neural network architecture for car state prediction while driving based on competitive learning. Competitive learning creates several duplicates of the baseline neural architecture and during training only updates the architecture with minimum loss. The experiments compare the competitive learning approach to a single baseline architecture on a driving benchmark task. The paper is understandable but could benefit from some copy editing. 

The competitive learning approach seems rather adhoc and this paper feels quite incomplete without significant discussion and comparisons to ensembling. Much recent work has shown that duplicating and ensembling neural architectures can produce gains, and it’s not clear why competitive learning is better than ensembling, it seems less theoretically sound to me.

There is a huge confound in the experiments due to the competitive learning architecture having many more free parameters than the baseline architecture. Again I think comparing to ensembling with the same number of architectures duplicated and perhaps comparing to a single baseline with larger hidden layers to make the total number of free parameters comparable is critical to validating the proposed approach.

The graphical model of the driving process depicted in figure 1 seems nonsensical. If e is observed then all variables are known given the dependencies shown. Further, it is at best very poor notation to say that the driving action d decided at time t affects the vehicle state s at that same time. It should be that s_t depends on d_(t-1). Also, according to this figure the driving decision d does not depend on the observed vehicle state x which also seems invalid.

Odd to have a paragraph break in abstract

Figure 1 caption should include a brief explanation of the variables shown

review_title: null

final_score: rating: null
review_content: - In Figures 6 and 7, what do predictions correspond to? Is it the prediction 3 seconds after the last observed state, or the prediction right after the last state? In other words, after 6 or 1 steps?

- How do predictions degrade when predicting more steps, say 1, 6, 12, 24?

- In Figure 7, how much switching occurs between competitive layers? Is it sticking to the same layer for a couple steps, few seconds or for the whole sequence to adapt to a particular driver's style?
review_title: Details

final_score: rating: 4: Ok but not good enough - rejection
review_content: Authors propose a competitive learning architecture that learn different RNN predictors independently, akin to a committee of experts which are chosen with a hard switch at run-time. This work is applied to the task of predictive different driving behaviors from human drivers, and combines behaviors at test time, often switching behaviors within seconds. Prediction loss is lower than the similar but non-competitive architecture used as a baseline.
It is not very clear how to interpret the results, what is the real impact of the model. If behaviors switch very often, can this really be seen as choosing the best driving mode for a given situation? Maybe the motivation needs to be rephrased a little to be more convincing?
The competitive approach presented is interesting but not really novel, thus the impact of this paper for a conference such as ICLR may be limited.
review_title: Review

final_score: rating: 2: Strong rejection
review_content: 
This paper introduces a neural network architecture and training procedure for predicting the speed of a vehicle several seconds into the future based on video and vehicle state input. The architecture allows several RNNs to compete to make the best predictions, with only the best prediction receiving back propagation training at each time step. Preliminary experimental results show that this scheme can yield reduced prediction error.

 It is not clear how the best-performing RNN is chosen for each time point at test time. That is, how is the “integrated prediction” obtained in Fig. 7? Is the prediction the one with minimum error over all of the output layers? If so, this means the prediction cannot be made until you already know the value to be predicted.

It seems possible that a larger generic RNN might be able to generate accurate predictions. If I understand correctly, the competitive architectures have many more parameters than the baseline. Is the improved performance here due to the competitive scheme, or just a larger model? 

A large amount of additional work is required to sustain the claim that this scheme is successfully extracting driver ‘intentions’. It would be interesting to see if the scheme, suitably extended, can automatically infer the intention to stop at a stop sign vs slowing but not stopping due to a car in front, say, or to pass a car vs simply changing lanes. Adding labels to the dataset may enable this comparison more clearly.

More generally, the intention of the driver seems more related to the goals they are pursuing at the moment; there is a fair amount of work in inverse reinforcement learning that examines this problem (some of it in the context of driving style as well).

review_title: Unclear whether the experimental comparison is fair

Meta-review: The authors present a prediction framework that involves multiple 'competitive' RNNs, and they claim that they are predicting human intention. It is unclear if this method, which seems quite ad-hoc, is any different from a simple ensemble approach, and it is unclear that the model is predicting human intention. The experiments do not adequately demonstrate either.
Decision: Reject