Hi all,

Let’s say I have a dataset with a mix of continuous and categorical variables and I’m creating an imbalanced binary classification model. A certain categorical variable has many (1000’s) of non-ordinal values. This feature is very important, certain values have high success rates ( num success(num rows with value and flag of 1)) / num instances (all rows with value) ). I have created features that include cat_var_num_success, cat_var_ success_rate, and a feature that is a score of each value of the categorical feature. The score assigns the mean overall success rate as the score if the value low sampling, if the value has a sufficient number of observations, and the success rate is greater than the mean overall success rate, the score is raised, the score is lowered if the value performs worse than the overall mean.

These generated features have proven to be highly predictive and improve the model (xgb). My concern comes from the fact that I have calculated these values using the whole dataset, which I subsequently split into train-test. I am afraid that the performance of the model is increasing due to information testing information leaking into training via the generated features.

Should I create an additional holdout set which does not contribute to the calculations for feature generation and test on that?

Thoughts?

Any feedback is appreciated!

Hi I just published a blog post on how to train a text classifier using pytorch-transformers using the latest RoBERTa model. Colab notebook is available: https://rsilveira79.github.io/fermenting_gradients/machine_learning/nlp/pytorch/text_classification_roberta/

http://ruder.io/state-of-transfer-learning-in-nlp/

This blog post by Sebastian Ruder is a quick review of how natural language processing has benefited from transfer learning. He ties together how recent advances (e.g., pretrained models/BERT, optimization schemes, multitask fine-tuning, etc) can work together to improve language modeling, and also poses some open problems in the field. See also the (somewhat empty) HN discussion.

Hello lovely inhabitants of r/MachineLearning. I am doing a residency at Artificial Intelligence and Data Science Research Lab, Makerere University, Uganda. We are collecting speech data for use in analysing radio programs related to agriculture, we will use this data to help in mapping and understanding the spread of crop disease.

Help us out by taking some time and recording some words.

Go here to help us record or there to view our work.

I was thinking about why the training process for image recognition is such a resource intensive task, and I got caught up on the idea that information loss caused by using projections (i.e. pictures) as training data would be an interesting area to explore.

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In comes mesh based 3d models. The training subject now has all of the necessary features in a single example My question is twofold. Do you see any potential in using 3d models as a source for an ML algo/NN, and if yes, how would you go about doing it?

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I’ve done a bit of brain storming, but I can’t really get anywhere. My initial thoughts were decompose the problem into parts, and solve it from both ends. One challenge is getting an isolated object from an image to use as a target, and the other is decomposing a 3d model into a useful 2d projection that can be compared with the target.

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Figuring that the 2D part would be easier, I decided to tackle that first, and I found the task of meaningful image segmentation to be a bit more difficult than I expected. It essentially came down to this: I can use pre existing methods to segment an image, or I can make my own; but nothing actually does decent job of pulling out objects with the exception of NNs which need to be trained to do so. In an attempt to improve the training process, I essentially run into the need to use methods that I’m trying to replace.

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The 3D part has a variety of challenges as well. I figured I could do something like take an image of the model that’s in similar proportion to the image that contains my target, then try to align the centroid of the model with the centroid of the target. This would eventually get to needing to iteratively translate, rotate, and scale the model. Some other obvious issues include segmenting the 3d model, posing the model, and finding what model to use as a reference when there are multiple ones saved.

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Thoughts?

We have just released a new version of AmpliGraph. It is available from pypi, all you have to do is run pip install –upgrade ampligraph. It requires TensorFlow 1.13 or 1.14 to be installed.

For beginners, we have two new tutorials: * Basics with Game of Thrones data * Clustering and classification with football data

For advanced users, we have a new knowledge discovery API, including discover facts, clustering, near-duplicates detection, and topn query.

We also provide state-of-the-art results on two benchmark datasets (WN18RR and YAGO3-10), plus competitive results on other benchmarks. These results are fully reproducible.

The updated documentation with the complete changelog is available here: http://docs.ampligraph.org

If you have any questions or feedback, you can comment here or raise an issue on GitHub.

Hi all,

I was checking out this video about an ‘AI-enabled speech pronunciation coaching app’ where it records your voice and gives feedback on your (English) pronunciation. With details on which part of the word needs more practice. Please watch a snippet of the video demonstrating it here.

Can someone recommend me papers/repositories explaining the algorithms and method that (most likely) enables the app to do exactly this?

My take on it will be using ASR (Automatic Speech Recognition) to do speech to phonemes and phonemes to a special grammar (e.g. ARPABET). Then the recognised speech, in the special grammar, is compared to the truth. If it matches, then your pronunciation is perfect. Otherwise, it will give you feedback on the specific part of the word that needs practice.

Thanks!

I’ve been seeing the Intel NUC 8 mini PCs going on sale. Specifically the NUC8i7HNK2 with an i7-8705G 3.10 Ghz, Kaby Lake G processor and a Radeon RX Vega M GL is on sale for $700 (pretax) without the RAM or SSD.

This is a great value! I need a portable machine learning teaching tool. A big bulky gaming GPU laptop is not that great (unwieldy, price and performance wise). These Intel NUCs you can just throw in the luggage and plug it in anywhere to do a demo or try out an idea before I go out and rent a cloud GPU to do the heavy lifting.

But is that RX Vega M GPU any good for machine learning?

I know last year they didn’t support CUDA but is this still an issue with libraries? I tried digging into documentation but it’s unclear.