I think building a model to learn a specific musician’s style could be a fun side project and was hoping to get some thoughts. I have complete multi-track MIDI discographies for a few bands, but I’m struggling to figure out the best way to structure the input/output of the model.

The general idea is to feed in some set of instrument/vocal tracks (this selection will basically be a hyperparameter) and generate the target track as an output. An RNN seems like the obvious approach and I was planning to start with a BLSTM using Keras and music21 in Python. To generate the training data, I’ll use a training set of the songs to randomly sample 10 second (another hyperparameter) clips. The same track will always be the target, and I am planning to use a consistent subset of the other tracks as the input. However, it would be nice to eventually use a varied set of input tracks to generate the output.

Does anyone have experience doing something similar to this, or have any relevant papers/libraries to recommend?

I hope I am positing in the right place. If not, i’ll take the post down.

I am a university researcher working on a project that involves matching names with biographical data from SEC filings. I downloaded all the filings for the organizations I am interested in, and wrote a Python script that basically finds officers’ names in the document, and then looks for gender, age, education and job title. It is tricky because companies can have different formats for these documents, so you have to think about different possibilities the data can be presented.

I use a fuzzy string matching to account for differences in spelling and typos, and different ways of naming tables. But most of the “learning” came from me manually tuning the script. Unfortunately, the script has to do a lot of safety checks to avoid outputting jibberish data (e.g. assuming a list of company names are people, etc). Finding age is also very tricky, as you often have to parse sentences to search for patterns such as “Mr. ABC, age 56” or “Mrs. Jean B. XYZ, MD, 46.”

My script works well and outputs the data that I want. The main issue that I have is that it takes a good 30 seconds for all the calculations to be made for just one company (around 5-20 executives per company). The reason, I think, is because my script tests a lot of different possibilities, even if they are not applicable to the document.

I am sure I am not the only one working on extracting data from semi-structured documents. I wanted to know if I could get feedback on what strategies I could implement to improve my script. I would particularly be interested in methods that involve tracking the performance of each data parsing strategies so that the computer does not waste time using a method that doesn’t work so well.

Thanks a lot for your help!

Link: https://distill.pub/2019/advex-bugs-discussion/

Hi, one of the original paper authors here! Recently, Distill.pub hosted a community discussion of our paper, with commenters who reproduced, extended, and further discussed the results of our recent paper. Happy to answer any questions!

I’m working in IT (think somewhere between sysadmin/security and operations) at WWF Netherlands. A colleague of mine who’s involved in the ‘Early Warning System’-program asked me to help me spread the following.

WWF would like to invite you to participate in a Request for Proposals (RfP) to become a tech partner in the Early Warning System (EWS) program which entails the development of a machine learning model to predict the risk of illegal deforestation using big data.

The RfP process began August 5th. Please have a look at the full RfP with technical details on the website and the Terms and Conditions. We encourage you to submit your application when ready as we will be reviewing on an ongoing basis.

If this is not appropriate let me know! If you have questions, ask.. If you have really difficult question, still ask but I might have to check with one of my colleagues 🙂 Do not hesitate to share this, let’s use tech for good.

Hi everyone, we recently open source our NLP/speech project at ACL 2019 – DELTA, a DEep Learning based language Technology plAtform (https://github.com/didi/delta). It aims to provide an end-to-end solution for training, deploying, and developing natural language processing and speech models for both academia and industry use cases.

DELTA has been used for developing several state-of-the-art algorithms for publications and delivering real production to serve millions of users. It helps AI developers to quickly build models and easily deploy to production, featuring:

• Easy-to-use
  • One command to train NLP and speech models, including:
    • NLP: text classification, named entity recognition, question and answering, text summarization, etc
    • Speech: speech recognition, speaker verification, emotion recognition, etc
  • Use configuration files to easily tune parameters and network structures
• Easy-to-deploy
  • What you see in training is what you get in serving: all data processing and features extraction are integrated into a model graph
  • Uniform I/O interfaces and no changes for new models
• Easy-to-develop
  • Easily build state-of-the-art models using modularized components
  • All modules are reliable and fully-tested

For more details, check out: https://github.com/didi/delta. Thank you!

I’m training a CNN on high resolution satellite imagery. Because of hardware constraints, I’m using EfficientNetB0 (EB0) to predict classes from 800x800x3 tiles. This can be done thanks to the 2D GlobalAveragePooling layer at the end of the model which compresses the 25x25x1280 feature map (7x7x1280 when working with the original 224×224 images) into a 1D 1280 vector to be fed to dense layers and such. It works surprisingly well. Even more, I get better performance with EB0 used on 800x800x3 tiles than with EB5 applied on 456x456x3 tiles (the resolution for which they were designed).

How is this possible?

I was wondering what everyone thought were the best matched conferences for machine learning research applied to the natural sciences. From my research online, KDD and AAAI seems to be the best venues. Other venues such as ICLR seem to encourage applied research in their calls for papers but not to accept many applied papers (outside of a few chemistry papers).

Thanks for your help.

I am refactoring my data input pipeline from a custom set of classes, which need to be scrapped because the process is inefficient, error-prone, hard-to-use, and not scalable, to an end-to-end pipeline built using Tensorflow 2.0’s tf.data module. I understand the overall process, and how to use the module, but I have a few questions regarding how to properly structure the pipeline:

1. Should I use object-oriented design or functional design? Because I would intuitively go with the OO route, using abstract base classes, and eventually having each feature be it’s own sub-class, since my dataset doesn’t actually include any of my features, but rather just the files from which data is sourced to calculate the features. But in the examples I have seen, I don’t really see anyone implementing that sort of structure.
2. Should use the tf.feature_columns module to store each individual feature, and then concatenate these feature columns to get my output data? Or should I concatenate the individual feature tensors together and use this as my input data?
3. My model takes 3 different inputs – one consisting of numerical and categorical features, and two separate tokenized sequence inputs. Should I implement this by creating three instances of tf.data.Dataset, one for each input, or should I create one instance for all the data, and then just ‘pop’ or whatever the equivalent function is the two columns holding my sequence data? Or is it a matter of preference?
4. This one is more just to help me out and not necessarily having to do with tf.data pipeline structure. How do I implement a dynamic tokenizer for my sequence data, which will just update the vocabulary dict with the new word and assign it the next integer, so that the model can be trained continuously with new data, rather than having create a new tokenizer each time a significant amount of new words appear in the data, and retrain the entire model from scratch?

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If anyone could point to some good examples of pipelines like this, or just help me understand the way the TF designed this module to be used, and how I can use it most effectively, I would be most appreciative and send a virtual hug your way, Or order you UberEats – I’m dead serious lol, that’s how desperate I am to understand this. Because I have already planned everything out and how everything is supposed to work, but don’t want to start coding until I’m sure I’m structuring and using everything properly.

Cheers, and if you help me figure out the answers, I will literally DM you and get you UberEats (up to $20 max).

Social networks are quickly becoming the primary medium for discussing what is happening around real-world events. The information that is generated on social platforms like Twitter can produce rich data streams for immediate insights into ongoing matters and the conversations around them. To tackle the problem of event detection, we model events as a list of clusters of trending entities over time. We describe a real-time system for discovering events that is modular in design and novel in scale and speed: it applies clustering on a large stream with millions of entities per minute and produces a dynamically updated set of events. In order to assess clustering methodologies, we build an evaluation dataset derived from a snapshot of the full Twitter Firehose and propose novel metrics for measuring clustering quality. Through experiments and system profiling, we highlight key results from the offline and online pipelines. Finally, we visualize a high profile event on Twitter to show the importance of modeling the evolution of events, especially those detected from social data streams.

https://medium.com/ai%C2%B3-theory-practice-business/real-time-event-detection-on-social-data-streams-72f89f394fda

we’re hosting a live session this afternoon (6:30PM EST) to review XLNet, the recent high performing NLP model. feel free to join us if you’re available (there will be live chat available), but also feel free to post your thoughts and discussion points here and we’ll make sure to cover them https://aisc.ai.science/events/2019-08-06