Accuracy based on word position not included padding. 80% of the dataset to train, 20% of the dataset to test. Dataset is English-Vietnam, Trainset to train, validation and test set to test.

1. basic-seq2seq, test accuracy 10.34%
2. lstm-seq2seq, test accuracy 11.89%
3. gru-seq2seq, test accuracy 11.50%
4. basic-seq2seq-contrib-greedy, test accuracy 25.28%
5. lstm-seq2seq-contrib-greedy, test accuracy 33.09%
6. gru-seq2seq-contrib-greedy, test accuracy 31.28%
7. basic-birnn-seq2seq, test accuracy 12.55%
8. lstm-birnn-seq2seq, test accuracy 12.11%
9. gru-birnn-seq2seq, test accuracy 11.98%
10. basic-birnn-seq2seq-contrib-greedy, test accuracy 27.50%
11. lstm-birnn-seq2seq-contrib-greedy, test accuracy 34.25%
12. gru-birnn-seq2seq-greedy, test accuracy 32.58%
13. basic-seq2seq-luong, test accuracy 2.40%
14. lstm-seq2seq-luong, test accuracy 13.08%
15. gru-seq2seq-luong, test accuracy 7.35%
16. basic-seq2seq-bahdanau, test accuracy 13.22%
17. lstm-seq2seq-bahdanau, test accuracy 13.38%
18. gru-seq2seq-bahdanau, test accuracy 14.02%
19. basic-birnn-seq2seq-bahdanau, test accuracy 13.88%
20. lstm-birnn-seq2seq-bahdanau, test accuracy 13.16%
21. gru-birnn-seq2seq-bahdanau, test accuracy 13.47%
22. basic-birnn-seq2seq-luong, test accuracy 7.49%
23. lstm-birnn-seq2seq-luong, test accuracy 13.27%
24. gru-birnn-seq2seq-luong, test accuracy 13.76%
25. lstm-seq2seq-contrib-greedy-luong, test accuracy 45.52%
26. gru-seq2seq-contrib-greedy-luong, test accuracy 8.14%
27. lstm-seq2seq-contrib-greedy-bahdanau, test accuracy 43.88%
28. gru-seq2seq-contrib-greedy-bahdanau, test accuracy 44.13%
29. lstm-seq2seq-contrib-beam-bahdanau, test accuracy 24.49%
30. gru-seq2seq-contrib-beam-bahdanau, test accuracy 22.26%
31. lstm-birnn-seq2seq-contrib-beam-luong, test accuracy 24.15%
32. gru-birnn-seq2seq-contrib-beam-luong, test accuracy 22.32%
33. lstm-birnn-seq2seq-contrib-luong-bahdanau-beam
34. gru-birnn-seq2seq-contrib-luong-bahdanau-beam
35. bytenet-greedy
36. capsule-lstm-seq2seq-contrib-greedy
37. capsule-gru-seq2seq-contrib-greedy
38. dnc-seq2seq-bahdanau-greedy
39. dnc-seq2seq-luong-greedy
40. lstm-birnn-seq2seq-beam-luongmonotic, test accuracy 27.23%
41. lstm-birnn-seq2seq-beam-bahdanaumonotic, test accuracy 26.34%
42. memory-network-lstm-seq2seq-contrib, test accuracy 28.02%
43. attention-is-all-you-need-beam, test accuracy 37.80%
44. conv-seq2seq, test accuracy 33.73%
45. conv-encoder-lstm-decoder, test accuracy 32.91%
46. dilated-conv-seq2seq, test accuracy 33.17%
47. gru-birnn-seq2seq-greedy-residual, test accuracy 34.35%
48. google-nmt, test accuracy 33.09%
49. bert-multilanguage-transformer-decoder-beam, test accuracy 44.69%
50. xlnet-base-transformer-decoder-beam, test accuracy 28.83%

Link to repository, https://github.com/huseinzol05/NLP-Models-Tensorflow/tree/master/neural-machine-translation

Link to dataset, https://github.com/stefan-it/nmt-en-vi#dataset

Discussion

1. Based on 20 epochs only.
2. Accuracy based on word positions.
3. Some results are empty because the models are slow to train, still waiting for the results.
4. Sort from shortest length to longest length and do bucketing from it will improve the accuracy.

In a Convolutional Layer of a given Convolutional Neural Network there is a defined input of size NxMxD. Whereas N and M stand for a dimension of an input image (can be smaller than the original size of an image due to pooling) and as I understand, D stands for a number of filters used in Convolution. My question is how network decides, what are the best filters for a given layer?

I want to mine an open data source do identify emerging and growing food concepts as title says. Concepts such as ingredients, flavor, spices etc. Do you know any source that can provide me with such information?

​

So far I found https://www.wired.com/story/how-grubhub-analyzed-4000-dishes-to-predict-your-next-order/ this article. I did some digging but I couldn’t find the data set they used. That was the closest I could get.

Hi,

I got some doubts about transfer learning.

I have this pre-trained model X, trained on the COCO dataset (its a dataset made of images; cars, persons, apples, etc…), and I want to retrain it using transfer learning with a new dataset made of one object that’s already part of the COCO dataset: apples. I’m doing so because I want to use my apple dataset, and not the COCO one.

Now comes my doubt. What will happen with the previous knowledge the model has about apples? Will it differentiate my apples from COCO’s? Also, what if I re-label my apples to “manzanas” (apple in Spanish), will there be some sort of conflict between the “manzana” and the already known “apple” label?

Hope the question makes sense. Thanks.

Hello,

I posted in this subreddit regarding a proposal for my undergraduate capstone project and got some feedback pointing out that perhaps stock prediction isn’t very well suited for what I was studying.

I wanted to know if you could recommend some subjects or areas you’d find interesting to research and build a project about using machine learning. Especially using newer models such as CNNs and RNNs or other state-of-the-art algorithms, since that’s what my school expects the project to cover.

Thank you.

This is a place to share machine learning research papers, journals, and articles that you’re reading this week. If it relates to what you’re researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you’ve read.

Please try to provide some insight from your understanding and please don’t post things which are present in wiki.

Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links.

Previous weeks :

Most upvoted papers two weeks ago:

/u/zephyrzilla: https://arxiv.org/abs/1908.03770

/u/Moseyic: Exploration by Disagreement

Besides that, there are no rules, have fun.

For the past month I have been working on voice conversion using unpaired data. I naively applied image conversion algorithms to audio spectrograms and after working out a few obstacles I got convincing, although not perfect, results.

Using the exact same algorithm on music genre conversion is also possible and the results, despite a fairly shallow generator with very low capacity, are pretty interesting.

Here are some examples:

https://youtu.be/3BN577LK62Y

The model is able to translate audio signals of any length and does not use any vocoder.

I cannot find papers with similar approaches, and I don’t really know what I should do with this research. Being an Engineering student and not understanding how the academic world works, maybe a simple article and a code release is the best idea.

Thank you for your attention!

Content Update in PyTorch NLP Tutorial repo.

– Text Classification, with simple annotation.

• Dataset: HuffPost news corpus including corresponding category.
• Pre-trained word vectors: How pre-trained word representations affect model performance (via ablation study)

The model trained on this dataset identify the category of news article based on their headlines and descriptions.

https://github.com/lyeoni/nlp-tutorial/tree/master/news-category-classifcation

Hey. I have a degree in Computer Science with a focus on AI and ML. I truly believe we can do more to popularize the concept of AI. I will be doing this through simulations were AI learns to deal with a simple problem and gives an answer to a specific question. I created the first one. Please take a look if you’re interested. Feedback and ideas for the next one would be great 🙂

https://www.youtube.com/watch?time_continue=1&v=l2jVMfOhTKY

I am thinking of segmenting based on keywords and afterwards using Named Entity Recognition finetuned for each segment to extract different fields like:

• Experience: company name, job title etc.
• Education: school, major etc.

However I wanted to see if there is a better approach for this problem. Any recommendations/references is welcome!