Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: - This paper investigates hierarchical classification using deep nets during learning and a greedy MAP procedure during inference. 

- While this paper is trying to address an interesting subject, the paper is very confusing to read and needs a lot of improvement in explaining its content before it can get published.

- "A combined cost allows travelling of information across levels which is equivalent to transfer learning between levels". What happen to the results when the network architecture does allow shared cost function, but while keeping the downpour algorithm intact? 

-  The experimental section should briefly mention the task description and put the results into context w.r.t alternative methods (other than just the flatten network)

review_title: hierarchical classification

final_score: rating: 5: Marginally below acceptance threshold
review_content: In this paper, the authors propose a hierarchical classification method using neural networks. Traditional techniques that consider the hierarchy while training are inefficient to train. Other techniques aim to solve this inefficiency by flattening the labels, which results in ignoring the hierarchical relationships between the labels.

The main problem with the paper is with its presentation. I did not completely understand how the neural network captures the hierarchy of the structure and what each depth is presenting? Does each nn depth correspond to a level in the hierarchy? How do the nodes indicate the predictions and which nodes are responsible for prediction?

I did not understand how the neural network architecture is designed? Is it a feedforward as shown in the figures? How does it relate to the hierarchy of classes?

The authors mention that training hierarchical SVM's are difficult. Can they mention in what sense are these networks difficult to train?

There are some typos in the paper. For example:
  - Page 1, Models such as hierarchical SVM become ...
  - Section 2.2, Line 3 - The sentence is incomplete 
  - Page 3, We compare HiNet with a Flatten Network which has ...
and so on

I suggest re-writing the paper to make it more clear and understandable.


review_title: This paper proposes a neural network based approach for hierarchical classification

Decision: Reject