Some guidelines:
* Bonus points should be given only to exceptional projects. Please do not give bonus points easily.


Section 1. Problem and data description (3 pts)

1.1. The report should describe the problem and/or research questions addressed in the project. (1 pt)

* It is unclear what problem the project tries to solve.
* The problem is described but some details are missing.
* The problem is described well.

1.2. Bonus: Is the problem novel and/or original? (1 pt)

* No
* Yes

1.3 Data description. (1 pt)
Describe data dimensionalities, number of training samples, the format used.

* The data is not described.
* The data is described but some details are missing.
* The data is described well.

1.4. Please describe what details were missing in the problem/data description.


Section 2. Method (6 pts)

2.1. Method description. (2 pts)

The report should describe well the model used in the project. If the model was covered in the lectures, it is ok to describe the architecture (such as, e.g., the number of layers etc) without going into details (such as computations in a basic convolutional layer). If the model was not covered in the lectures, you need to provide enough details so that your classmates can understand it without checking external references.

* The model is not described.
* The model is described well but some details are missing.
* The model is described very well. I could implement the model based on the description.

2.2. Choice of the model. (2 pts)

* The proposed model is not reasonable for the task.
* The model is reasonable but some choices are questionable.
* The model is suitable for the task.

2.3. Bonus: Is the model novel and/or original? (2 pts)

* No
* Partly
* Yes, the model deserves to be presented in a conference

2.4. If you think that the model is not perfectly suitable for the task, please write your suggestions on how the model could be improved.


Section 3. Experiments and results (4 pts)

3.1. Are the experiments described well in the report? (2 pts)

* The experiments are not described.
* Experiments are described but some details are missing.
* Experiments are well described. I could reproduce the experiments based on the description.

3.2. Performance of the proposed model (2 pts)

* It is difficult to evaluate the performance (there is no baseline or no demo for tasks that require subjective evaluation).
* The results are adequate.
* The results are impressive (either close to the state of the art or good subjective evaluation).

3.3. Suggest what could be improved in the experiments.


Section 4. Conclusions (1 pt)

4.1. Conclusions are adequate:

* No
* Yes

4.2. Optional feedback on the conclusions.


Section 5. Evaluate the code. (3 pts)

* The code is not attached.
* The code looks messy.
* The code looks alright.
* The code is clean.


Section 6. Overall feedback (3 pts)

6.1. Bonus: Are the results worth presenting in a conference?

* No
* No, but some aspects of the project were exceptionally good.
* Maybe
* Yes

6.2. Mention something that your classmate did well.

6.3. Mention something that your classmate could improve at.