Hi reddit,

I am currently working on a problem and I would like to ask for your advice on the best way to handle it.

So, the goal here is to predict the time of resolution of what we will call “incidents”. Namely, an incident is like an issue that is opened and needs to be fixed. The status of an incident varies with time – for instance, comments may be added or the priority may change from “Not important” to “Critical”.

My dataset looks like this : a row corresponds to the status of an incident at a given time. A single incident, identified by its ID, is then made of several rows. Some features do not change while others change every time there is a modification (see table below).

The idea would be to be able to give a prediction at any time in the life of an incident. I would like to make use of this sequential form to use recurrent neural networks, but I don’t know how to do it exactly.

There is a new row every time there is a change in status. So this means that the time between first and second row might be a day while the time between second and third row might be a week or a month.

I was thinking of treating the problem as a NLP problem, i.e. for a single ID, each row corresponds to one word (the embedded word). This would mean that the input to the RNN would be something like x = [x1, x2, …, xN] where xi = [xi1, …, xiM] a row of the dataset.

Would that make sense, and if not, how would you proceed ?

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Thank you and have a nice day.

Title: Active Annotation: bootstrapping annotation lexicon and guidelines for supervised NLU learning

Abstract: Natural Language Understanding (NLU) models are typically trained in a supervised learning framework. In the case of intent classification, the predicted labels are predefined and based on the designed annotation schema while the labelling process is based on a laborious task where annotators manually inspect each utterance and assign the corresponding label. We propose an Active Annotation (AA) approach where we combine an unsupervised learning method in the embedding space, a human-in-the-loop verification process, and linguistic insights to create lexicons that can be open categories and adapted over time. In particular, annotators define the y-label space on-the-fly during the annotation using an iterative process and without the need for prior knowledge about the input data. We evaluate the proposed annotation paradigm in a real use-case NLU scenario. Results show that our Active Annotation paradigm achieves accurate and higher quality training data, with an annotation speed of an order of magnitude higher with respect to the traditional human-only driven baseline annotation methodology.

Link: https://arxiv.org/abs/1908.04092

This is not my project, but I think it’s great and it hasn’t been shared here yet.

Introducing Handout

“Turn Python scripts into handouts with Markdown comments and inline figures. An alternative to Jupyter notebooks without hidden state that supports any text editor.”

In a sense this is the best of both worlds, as you get the visual benefits of notebooks while having your code run as scripts and make it much easier to reproduce results.

It would be interesting to hear from those that love their notebooks if a tool like this can be a real alternative? If not, why not?

Abstract:

The microscopic assessment of tissue samples is instrumental for the diagnosis and staging of cancer, and thus guides therapy. However, these assessments demonstrate considerable variability and many regions of the world lack access to trained pathologists. Though artificial intelligence (AI) promises to improve the access and quality of healthcare, the costs of image digitization in pathology and difficulties in deploying AI solutions remain as barriers to real-world use. Here we propose a cost-effective solution: the augmented reality microscope (ARM). The ARM overlays AI-based information onto the current view of the sample in real time, enabling seamless integration of AI into routine workflows. We demonstrate the utility of ARM in the detection of metastatic breast cancer and the identification of prostate cancer, with latency compatible with real-time use. We anticipate that the ARM will remove barriers towards the use of AI designed to improve the accuracy and efficiency of cancer diagnosis.

https://www.nature.com/articles/s41591-019-0539-7.epdf?author_access_token=BI9AOTsesmNoV2lSdpucn9RgN0jAjWel9jnR3ZoTv0PDGU3ZwysZtsN41a2fOgaoj4PRxjTvAHjSFrKF_S_mq4QNNV8dNoxAjytIQuVz9vdjplLQHUSEPiIo392MzIJY8fqxLKHC5vIwNpLLEoXMnA%3D%3D

Hi everyone!

Few months ago I released Notify17, a tool to generate mobile notifications from simple web requests.

I think redditors of this sub may be interested in using it when dealing with models training, or any similar long lasting tasks, where you need to wait a long time between each iteration.

The most basic concept would be that, as soon as a model training finishes, you can invoke a function to let yourself know that the event occurred, and get the notification on your phone/browser:

from notify17 import n17_raw n17_raw("RAW_API_KEY", 'Model training finished') 

I’m looking for feedback from people in multiple fields, because I believe that Notify17 could be useful in many multiple scenarios than just backend development.

I’d love to know if anyone of you finds this tool to be useful for the machine learning field and/or what could be added/changed to make it worth the use for you all.

P.s. also MATLAB and LUA examples are around. In any case, a cURL request is enough to trigger any notification, e.g:

url -X POST  "https://hook.notify17.net/api/raw/RAW_API_KEY"  -F title="Model $MODEL_NAME training completed" 

Thanks a lot, I’m here to answer any question about this post!

Alberto

Hello everyone,

I was trying to train an autoencoder which takes a melspectrogram as input and outputs the same melspectrogram. It’s a reconstruction task. However, the model seems to be generating random noise. It’ll be great if anyone could point me towards any relevant github repos/papers which solve this task.

Thank you! 🙂

I have been playing around with DeOldify this past week, which as been a lot of fun. During my testing, I colorized a video of a Tasmanian Tiger which managed to reach the front page and resulted in an article from Daily Mail… which was awesome! Anyway, while playing around, I realized it might be cool to visualize the colorization process by colorizing a hyper-realistic drawing time lapse. The hope was that using this method I could see when the network recognized a face, hair, eyes, etc.

Achieving this was pretty simple as I didn’t need to write any code myself. I found a great time lapse from a talented artist drawing Cara Delevingne, downloaded it (with permission) and colorized it. I put the two videos side by side, and I was done!

Here are the results!

I hope you guys find it interesting. It was a little less eventful than I expected, but still pretty cool to see!

Hi,

I’d like to share the following toy dataset with you which is readily available in form as raw videos and tfrecords:

The dataset consists of 90 000 color videos that show a planar robot manipulator executing articulated manipulation tasks. More precisely, the manipulator grasps a circular object of random color and size and places it on top of a square object/platform of again random color and size. The initial configurations (location, size and color) of the objects were randomly sampled during generation. Different from other datasets such as the moving MNIST dataset, the samples comprise a goal-oriented task as described, making it more suitable for testing prediction capabilities of an ML model. For instance, one can use it as a toy dataset to investigate the capacity and output behavior of a deep neural network before testing it on real-world data.

https://github.com/ferreirafabio/PlanarManipulatorDataset

Feel free to use, share and comment 🙂