Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 8: Accept
review_content: This paper proposed a learning algorithm to recover the events of using an appliance and as well as the location of the appliance in a home by using smart electricity meter and a motion sensor installed a home. In the model, the input is a window of electricity energy consumption and context and the output of the model is the location collected by the motion sensor. The appliance activation as the latent variables is learned using a autoencoder architecture. 
This work is very interesting and new to the energy efficiency community and non-intrusive load monitoring. This provide an alternative approach to understand how energy was used in a house and so could be used potentially for energy efficiency and malfunction detection of appliances. The data is also new to the community. The paper is well written and the experiments are clear to me. I have some more detailed concerns:
1) One thing is surprising to me: in the autoencoder, you want to learning the latent variables {Z_t, Z_{t,cat}}. It is surprising that for example Z_{t,cat} was exactly the appliance category. I suppose it was aided by the location information presented in P(l|y,c)? I had an experience training an autoencoder for energy disaggregation, but it never worked well because the latent variables Z could be arbitrary.
2) It would be more readable if you the model was provided with more details, for example, the detailed model for L_e(Z_{t,cat};\theta_{L_e}). Although p_{\theta_{L_g}} and L_g were defined in a similar way, they should be explicitly given.

review_title: Official Blind Review #1

final_score: rating: 3: Weak Reject
review_content: # Review ICLR20, Self-Supervised Learning of Appliance Usage

This review is for the originally uploaded version of this article. Comments from other reviewers and revisions have deliberately not been taken into account. After publishing this review, this reviewer will participate in the forum discussion and help the authors improve the paper.


## Overall

**Summary**

The authors introduce a new method for classifying which appliance was turned on by looking at the change in total household electricity consumption and tracking people's coarse positions in the house.


**Overall Opinion**

The paper is concise and interesting, but for now, I have to reject it because the same major things aren't clear to me:

- Why are you doing this, to begin with, i.e. "... to optimize energy consumption"?. What does energy consumption have to do with classifying which appliance was turned on? There might be the rare edge case of a fridge door left open accidentally but that can't be the main argument here, can it?
- Now, assuming that there is a benefit to this, why can't we use the smart plugs all around? You already use them to provide ground truth. But I imagine they are cheap and easy enough to distribute at large scale, whereas the location sensor was custom-built by you if I understood correctly and probably has to be calibrated for every house.

Other than that, the paper is pretty good. Here are some minor...

## Specific comments and questions

- I got slightly disappointed when I picked up the paper because I was in a robotics mindset and assumed "Self-supervised learning of appliance usage" was some new way for a robot to analyze appliances and push buttons. I'd add a "human" in the title for clarity, e.g. "Self-supervised Learning of Human Appliance Usage" or "Self-supervised Learning of Appliance Usage from Human Location Data". But that's just my opinion, no pressure on this one.

### Abstract

- Please never repeat sentences from the abstract in the introduction or vice versa, good as they might be.

### Intro & Rel. Work

all good

### Problem Formulation

- Please always provide a proper caption for your figure so that the caption contains enough information for the reader to understand what's going on in the figure without having to search for it in the main text. This applies to Fig.1 and even more so to Fig.2 where I'd recommend explaining in broad strokes the information flow.
- Fig. 1 (a) Why is there higher power usage between 02-06 compared to 14-18 o'clock? Is that a heater as the outside temperatures drop overnight? (c/d) I don't think that's a good way of visualizing this. I don't get anything out of diagram (c) and I'd prefer it if you could encode the time aspect in the diagram (d) as color/alpha. E.g. blue agent's oldest position is almost imperceptibly white, growing bluer and darker as the data points get closer to the final step in the recording.

### Model

- Doesn't the softwax require to know the number of appliances a priori?
- How do you deal with $\alpha$ and $P_t$ when a person is at the edge of the visible space? Does this cause problems during training or did this never occur? I could imagine, since the location is noisy, a person could jump in and out of the visible area and cause training instability.

### Datasets

all good

### Results

- What's "Door / leaving" in Table 2? Which appliance is this?
- I think the standard at ICLR is to have the table and figure captions both below the respective objects
- The last 3 sections, 6.3 to 6.5, read a bit rushed and might need a rewrite for clarity. 
- Fig. 5 is nice. But shouldn't the signal add up to explain the entire scene? There's a bit of blur next to the couch. Is this a training set vs test set artifact?

### Conclusion

- This needs to be developed a bit more: what are some downsides to your method? Where does it NOT work or what makes it prone to error? And what are future directions based on this work? What's worth researching further or have you solved appliance use detection for good?

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I'm sure there are some good answers for my main concerns outlined at the beginning and after that, I'd be happy to adjust my rating. The rest are minor issues. Like I mentioned, save for page 8, the paper is well-written and focused.
review_title: Official Blind Review #2

final_score: rating: 6: Weak Accept
review_content: Authors proposed a multi-modal unsupervised algorithm to uncover the electricity usage of different appliances in a home. The detection of appliance was done by using both combined electricity consumption data and user location data from sensors. The unit of detection was set to be a 25-second window centered around any electricity usage spike. Authors used a encoder/decode set up to model two different factors of usage: type of appliance and variety within the same appliance. This part of the model was trained by predicting actual consumption. Then only the type of appliance was used to predict the location of people in the house, which was also factored into appliance related and unrelated factors. Locations are represented as images to avoid complicated modeling of multiple people.

As the final step, a customized clustering algorithm was used to turn appliance related events into clusters that each represent an appliance.

Authors trained the model with real world data collected from 4 homes over a few months each. Smart plugs and some human labeling served as ground truth. The results showed good performance of the proposed unsupervised algorithm in recovering the actual appliances in the homes.

Reviewer acknowledge the difficulty in collecting such data and the careful analysis into data characteristics which led to the algorithm design. Reviewer also found the illustrations (esp Fig 1) quite intuitive for understanding the problem. Proposed algorithm is quite reasonable. The release of code and data could also be helpful to the community.

Reviewer also saw some non-trivial issues that Authors may need to fix or explain:

1) Baseline setup seems to be too weak. Authors didn't explain how EL-KMeans was conducted which is non-trivial. The given citation (Arthur & Vassilvitskii, 2007) is a very generic paper discussing KMeans++, not EL-KMeans. In Reviewer's opinion this needs to be fixed for acceptance.

2) in Section 6.2 Ablation study, Author mentioned "we adopt a density propagation algorithm that only uses local neighborhood distance" but Reviewer didn't find any content related to the algorithm. Is this the cluster algorithm?

Other issues:

1) In table 2,  What happened to Home 4's hair dryer and rice cooker? Their F1 are as low as 1.1.

2)  The data size is quite small, which makes the conclusion less strong.

review_title: Official Blind Review #3

Meta-review: Authors proposed a multi-modal unsupervised algorithm to uncover the electricity usage of different appliances in a home. The detection of appliance was done by using both combined electricity consumption data and user location data from sensors. The unit of detection was set to be a 25-second window centered around any electricity usage spike. Authors used a encoder/decode set up to model two different factors of usage: type of appliance and variety within the same appliance. This part of the model was trained by predicting actual consumption. Then only the type of appliance was used to predict the location of people in the house, which was also factored into appliance related and unrelated factors. Locations are represented as images to avoid complicated modeling of multiple people.

The reviewers were satisfied with the discussion after the authors, and therefore believe this work is of general interest to the ICLR community.
Decision: Accept (Poster)