Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 7: Good paper, accept
review_content: The authors introduce the idea to train multi-task networks by constructing separate networks for different tasks and then putting a limit on the tensor-norm on shareable layers. In this ways it's not needed to explicitly design sharing (but different tasks still need to share the architecture). The proposed tensor losses are not differentiable, so to optimize them with SGD during training the author use sub-gradient descent. These are certainly interesting ideas which warrant acceptance. The presented results, improving accuracy on Omniglot from about 34% to about 36% are very weak though, considering that SOTA for deep learning (but using metric learning) is above 90% (e.g. from matching networks). Or is this not a fair comparison? In any case, the paper certainly warrants workshop acceptance.
review_title: Interesting idea but weak results (for now)

final_score: rating: 6: Marginally above acceptance threshold
review_content: ## Quality:
This is an interesting paper presenting a cute idea. It reads a bit like a "this didn't work as well"-alternative approach to its sister paper implementing the same idea with Tensor Factorisation, which is on ICLR main conference: https://openreview.net/pdf?id=SkhU2fcll
## Clarity:
Very clear, well-written.
## Significance:
This seems the biggest problem. The results are quite weak, clearly inferior to the sister paper.
Most importantly, why is the baseline omniglot STL accuracy around 0.34 while in the sister paper the accuracy for the same baseline appears to be around 0.65 in Fig4 top left? Am I missing something here?
In any case I believe that there should be a comparison against normal explicit sharing of the weights as baseline, which is easy to add in the plots.
Apart from that there is some smaller remarks that impact signficance:
+ there must be a lot of computational overhead to compute an SVD on each weight layer, which I assume needs to be computed after every weight update? What was the additional compute time?
+ The number of parameters is still the same as if these networks were trained independently, so parameter reduction is one advantage of hard explicit sharing which falls away here.

## Other remarks:
A relevant application of MTL is multilingual acoustic model training in speech.
See eg Scanzio et al 2008 (https://scholar.google.com/scholar?cites=2941155962830961778), which has all but the last layers shared, and Sercu et al 2015 (https://arxiv.org/abs/1509.08967) which is a CNN-based model and has multiple FC layers split.

Overall, PRO: cute idea, novel, well-written paper. CON: a bit too similar to sister paper on main track, weak results (and please clarify the difference in baseline?)
review_title: Review

Decision: Accept