As per object, what is the current state of the art? Last year we had some work on approximating both the solution and the PDE using neural networks:

https://arxiv.org/abs/1801.06637

This year (well, actually last year, too, but then the preprint kept being revised until recently) we had the paper from Google on approximating the solution given knowledge of the PDE (whose results are frankly not as impressive as advertised, solving the 1D Burgers equation with 1024 convolutions is not gonna give the scare to commercial CFD codes producers)

https://arxiv.org/abs/1808.04930

There must have been something else, of course, or NeurIPS wouldn’t have accepted a workshop on Machine Learning and the Physical Sciences. What’s the current state of the art? I’m especially interested in fluid dynamics, but I wouldn’t mind learning about using Deep Learning to solve PDEs stemming from other branches of physics.

Video here: https://www.youtube.com/watch?v=mUt7w4UoYqM

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I thought this was an amazing talk by Aleksander Madry on what adversarial examples really are; a problem with the data we train our models on. That’s one reason why they generalize.

Driven by progress in deep learning, the machine learning community is now able to tackle increasingly more complex problems—ranging from multi-modal reasoning to dexterous robotic manipulation—all of which typically involve solving nontrivial combinations of tasks. Thus, designing adaptive models and algorithms that can efficiently learn, master, and combine multiple tasks is the next frontier.

Video: https://slideslive.com/38917690/multitask-learning-in-the-wilderness

Good Luck everybody!

Posted originally in /r/MLQuestions to no avail. Please delete if this doesn’t belong here.

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My problem is a bit unique I think, though in all probability it’s my naivety with ML and I just don’t know if this is a solved problem.

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We are processing standard forms from scanned images using some OCR techniques, and have a JSON output that basically shows something like this:

"3": {"fieldValue": "TRUCKS CARS AUTOMOBILES 5000"},

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The above comes from a field called “TRUCKS CARS AUTOMOBILES” and the value that’s entered in for that particular form field is “5000”. The OCR cannot separate the form label from the form value, so we need to parse this. Initially we tried to regex every field value out, but this proved to be too brittle, as our OCR does not perfectly recognize text; words like ‘address’ might output as ‘addrefss’ etc. Next I tried to use the python library fuzzywuzzy to do fuzzy string replacement instead of pure regex. The result was far better but there are still many edge cases that I can’t account for considering the nature of these forms are varied and sometimes of poor scan quality. We have many different types of fields and values, for example another field looks like this:

"455": {"fieldValue": "ADDRESS (CO NAME AND PLACE) COCONUT FACTORY 12345 COCO STREET MIAMI FL 86884"},

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The upside is that we have a JSON file that also corresponds to the above JSON with data labeling of sorts, which is why I initially tried to regex, and then fuzzywuzzy. Here is an example corresponding JSON to the extracted value above:

"455": {"label": "ADDRESS (CO NAME AND PLACE)"},

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My thought is to use an NLP library like TextBlob or Spacy to somehow classify labels, and then extract the remaining portion of the string.

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What is the best approach to do this? Thanks!

I used Unity3D to make an AI jet that learned how to bomb a missile launcher. It used deep reinforcement learning and PPO.

I put the results together into a short video you can see here: https://www.youtube.com/watch?v=iP28kOCpW94

If any of you want to see the source you can download it here: http://www.mediafire.com/file/n120iqtrynlmr07/AI_Jet_Bomber.zip/file

Hi everyone,

at the beginning of this week we uploaded our manuscript on large-scale hydrological modeling using LSTM-based models. Maybe you find something interesting/useful in it. If you have any questions regarding this work, feel free to ask.

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Abstract:

Regional rainfall-runoff modeling is an old but still mostly out-standing problem in Hydrological Sciences. The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone. In this paper, we propose a novel, data-driven approach using Long Short-Term Memory networks (LSTMs), and demonstrate that under a ‘big data’ paradigm, this is not necessarily the case. By training a single LSTM model on 531 basins from the CAMELS data set using meteorological time series data and static catchment attributes, we were able to significantly improve performance compared to a set of several different hydrological benchmark models. Our proposed approach not only significantly outperforms hydrological models that were calibrated regionally but also achieves better performance than hydrological models that were calibrated for each basin individually. Furthermore, we propose an adaption to the standard LSTM architecture, which we call an Entity-Aware-LSTM (EA-LSTM), that allows for learning, and embedding as a feature layer in a deep learning model, catchment similarities. We show that this learned catchment similarity corresponds well with what we would expect from prior hydrological understanding.

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TLDR;

• Single LSTM-based model is trained on meteorological time series + static catchment attributes to predict river runoff for hundreds of catchments.
• Outperforms a large set of hydrological benchmark models (calibrated by independent groups) significantly, even in unfair settings (hyd. models calibrated for each catchment separately vs. LSTM model calibrated for all at once).
• Proposed LSTM adaption (Entity-Aware-LSTM), where static features are used to modulate the input gate and all remaining parts of the LSTM only receives recurrent + dynamic (meteorological time series) input.

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Manuscript: https://arxiv.org/abs/1907.08456

Code + Data: https://github.com/kratzert/ealstm_regional_modeling