We present MMDetection, an object detection toolbox that contains a rich set of object detection and instance segmentation methods as well as related components and modules.

• collaboration with 10+ research institutes
• 20+ supported methods
• 200+ model weights

Technical report: https://arxiv.org/abs/1906.07155

A simple tutorial on how to apply pre-trained BERT model to Korean QA task.

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A pre-trained BERT model is publicly available !

huggingface/pytorch-pretrained-BERT repository contains op-for-op PyTorch reimplementations, pre-trained models and fine-tuning examples for Google’s BERT model. And as a result of submission BERT fine-tuned with default hyper-parameter, it ranked 30th with EM= 71.47, F1= 89.71 on the KorQuAD leaderboard.

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So, I covered the process of fine-tuning and submitting BERT and result for official evaluation on KorQuAD. Once your BERT model has been evaluated officially, scores will be added to the leaderboard.

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https://github.com/lyeoni/KorQuAD

I’ve been trying to get back into a more ML/science based role (currently I’m more on the tech business side). Within my own specific domain, I know all of the major algorithms and have been able to shine in that particular topic (times series and regression models). When it comes to generic data science, I have been able to handle myself quite well on most fronts (probability questions, conceptual questions, what is the central mean theorem? can you explain MLE? etc…) .

One topic kept coming up though, with 15 out of the 17 interviewers, across all 5 companies (including two of the biggest names in tech) asking this exact question:

Suppose you have a binary classifier (logistic regression, neural net, etc…) how do you handle imbalanced data sets in production?

I don’t know 🙁 . I know that you need to be careful with which metric you use to evaluate your model, that you should look at precision and recall or the ROC, instead of just accuracy. And that your sampling strategies should change to better reflect each class. But all of this is during training.

Once in production, I know that you face a catch-22 situation:

• If you don’t skew your training data, then you don’t have enough data from the sparse class for the classifier to learn something, and it will just learn to always predict the dense class.
• If you do skew your data, then now you’re facing a situation where the distribution of the training data and the distribution of the production data are completely different, so your model won’t predict well (at least my understanding is that different distributions in test and in prod is always a recipe for disaster).

Is my assessment of the dilemma correct? And how do you solve it?

Why is this question so popular (FWIW – none of these companies were doing medical or security applications….)

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Some follow up questions and/or hints that were given (but I still couldn’t really answer the question in a satisfactory way):

• If this is the case, but only you noticed that your binary classifier is not performing well only after you have already deployed it in production and had been scoring it for a few weeks, what do you do? (My answer, go back to training, and either re-evaluate which features you want to use, or find more data to train on) , second follow from the same person: What if I told you that you are stuck with the same model and couldn’t get any more data, what do you do then (I answered: l1 or l2 regularization? but these are applicable to any data set, they aren’t specific to imbalanced data. Fiddle with the K in your K-fold CV? that wouldn’t work either — by this point I felt like I was being Kobayashi Marued…)
• Can you adjust your classifier after training, but before deploying it, so that it is adjusted to the original distribution, not the skewed (downsampled or upsampled) distribution you used during training? (Drew a blank – as far as I know, any adjustment to the model based on knowledge prior to deployment constitutes training in one form or the other….)

With regards to the second question, I did come across [this thread and the blog that it linked to](https://stats.stackexchange.com/a/403244/89649) . It applies only to logistic regression, not any other binary classifier as far as I can tell . What about other classifiers? (Or is it that logistic regression is the only applicable algorithm in the imbalanced case?)

Ran into this blog post on Google Cloud blog. A bit surprising they are pushing evolution rather than TPUs / deep learning.

Reinforcement learning (RL) has become popular in the machine learning community as more and more people have seen its amazing performance in games, chess and robotics. In previous blog posts we’ve shown you how to run RL algorithms on AI Platform utilizing both Google’s powerful computing infrastructure and intelligently managed training service such as Bayesian hyperparameter optimization. In this blog, we introduce Evolution Strategies (ES) and show how to run ES algorithms on Google Kubernetes Engine (GKE) …

https://cloud.google.com/blog/products/ai-machine-learning/how-to-run-evolution-strategies-on-google-kubernetes-engine

https://github.com/lerrytang/es_on_gke

Hey everyone, Chuan Li, our Chief Science Officer at Lambda put together a series of five TensorFlow 2 tutorials:

Basic Image Classification: https://lambdalabs.com/blog/tensorflow-2-0-tutorial-01-image-classification-basics/

Transfer Learning: https://lambdalabs.com/blog/tensorflow-2-0-tutorial-02-transfer-learning/

Checkpoints: https://lambdalabs.com/blog/tensorflow-2-0-tutorial-03-saving-checkpoints/

Early Stopping: https://lambdalabs.com/blog/tensorflow-2-0-tutorial-04-early-stopping/

Distributed Multi-node Training: https://lambdalabs.com/blog/tensorflow-2-0-tutorial-05-distributed-training-multi-node/

Let us know what you think. Hope that you find these to be helpful!

Future tutorials will be posted here: https://lambdalabs.com/blog/tag/tutorials/

So this is obviously hypothetical and entirely speculative on my part as an ML hobbyist so I’m sure I’m missing something and will get downvoted for my dumb hypothetical.

But here it goes.

Meta-learn

Lets say you have a Neural Network (the parent network) which can design arbitrary children networks and learn optimized design patterns for a given task. Of course your parent network won’t be super generalized for any type of network design, just relatively specific tasks. This is kind of why we don’t have “real” AI or AGI. The tasks are still relatively narrow.

Skimming the literature on meta-learning it looks like researchers have been able to get SOME generalization by training their meta-networks on multiple tasks. But of course data and identifying tasks might be a limitation for scale-ability and high levels of generalization. So I purpose a potentially more elegant way.

PowerPlay

This is where Jürgen Schmidhubers PowerPlay would come in. The PowerPlay algorithm is split into a solver and a problem generator. The problem generator generates novel problems which the solver has to try to solve. Novel problems are problems which are unsolvable by the current solver. The created problems are just a bit more complicated than the most complicated solvable problem. The solver has to be able to solve all previous problems the generator created plus the new one.

Meta-PowerPlay

Both the problem Solver and Generator have parent Networks which continually learn to design more sophisticated Solvers and Generators until you have much more general problem solvers, or rather a neural network that can design general problem solvers.

Both the Solver and Generator have meta-networks that find their optimal design patterns.

Step 2: ???

Step 3: profit!

https://github.com/timesler/facenet-pytorch

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Hi all, this project contains pytorch pretrained inception resnets ported from the davidsandberg/facenet github repo. Models are implemented and used according to the standard pytorch/torchvision methodology (inheritable model modules, torchvision style model zoo for downloaded/cached pretrained state dictionaries etc.). Currently, the project covers face detection using MTCNN and face recognition. MTCNN is implemented as a single stand-alone pytorch module that wraps the p-, r-, and o-net modules as well as the post-processing, making it easy to chain MTCNN and recognition resnets together in a face recognition pipeline.

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The motivation for the project was the lack of a clean implementation in pytorch that provides the performance of the davidsandberg/facenet github repo. My aim was to build a project that could be easily used to add value existing pytorch projects without a great deal of effort.

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Performance wise, I see similar or better inference speed on my local machine when compared to the original repo, but that one data point doesn’t say a hell of a lot. Any extra testing or feedback much appreciated.

A new approach to generating recipes directly from food images that produces more compelling recipes than retrieval-based approaches, according to human judgment. Evaluated on the large-scale Recipe1M data set, this approach improves performance with respect to previous baselines for ingredient prediction. With this work, we aim to provide access to the preparation of a meal simply by inputting a food image.

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https://ai.facebook.com/blog/inverse-cooking/

I am a Masters student and have been struggling with motivation for some time.

Just a few days ago, I had what I thought was a really interesting idea. I performed a few small experiments and confirmed my hypothesis. Then when I started to look for existing work/theoretical foundations I found the exact same idea has been worked on and been put up on arxiv just a week ago.

This has happened to me multiple times and is a bit demoralizing and I just give up on the idea altogether thinking, what’s the point, even if i improve upon this idea, it’s not unique or novel in any way.

Has this happened to anyone else, do you work on the idea further? Or do you just give up and look for different ideas ?