I have a problem that I’m looking to solve and want some direction.

I have data that I need to process and I have handlers that I need to rank. The handlers are essentially people that may be able to process each piece of data. Each piece of data that comes in is sent to the handlers for them to process. If they are unable to process it, then I send it to the next handler. I do this until one of the handlers successfully processes it or I have exhausted all the handlers.

I have several criteria that I’m using to rank the handlers. For example, how frequently they successfully handle a piece of data, how long they take to process the data, and a score for how well they handled it (successful solutions can be graded and although any is acceptable, we would prefer the one that produces the better answer).

Given a bunch of data with the above statistics, I would like to do two things. First, produce a report that ranks the handlers. This is currently done manually so I would like to automate this step. Second, I would like to have the dispatcher respond in real time to changes in the statistics. For example, if one of the handlers starts taking longer than normal then we should deprioritizing subsequent requests to that handler until they improve.

Are there recommendations for a toolkit or a subset of algorithms that I should be researching? Any pointers are appreciated.

Meta-learning is good for learning new class with <10 samples.And it requires sort of pre-training with similar classes.

Is there good recent works to improve convergence for randomly-initialized networks using meta-learning? Last time I looked into https://ai.google/research/pubs/pub46116/ and rejected work at openreview,
So far results are worse than SGD and Adam. Or maybe ~0.1% faster convergence but consumes ~30% more computations.

I recently wrote about, how to use a ‘imagenet’ pretrained efficientNet implementation from keras to create a SOTA image classifier on custom data, in this case the stanford car dataset. There are some details about BatchNormalization and how to start by training only the classifier layer and later train the complete network. But in the end it’s a good starter for beginners (using the jupyter notebook).

http://digital-thinking.de/keras-transfer-learning-for-image-classification-with-effificientnet/

If you are looking for a PyTorch example (not mine):

https://github.com/morganmcg1/stanford-cars

I’ve been wondering about this question:

• On one hand, conventional wisdom has that winning a Kaggle competition is quite a feather in your cap and it will open all sorts of doors for you. You will have to fend off recruiters with bear spray, given the amount of corporate attention you will receive once you win.
• On the other the few Kaggle winners that I follow personally (connecting on LinkedIn, following their blogs, etc…) don’t seem to have their careers impacted by their achievements. You don’t see them switching to Google or FB or something a few months after they win. They all stay in the relatively obscure tier 2 role they worked in. Sometimes not even that, they turn out to be freelancers and they remain that way, or something like that….

Any thoughts on what is the more accurate depiction of Kaggle winners?

An introduction / crash course about TensorFlow 2.0, as a colab notebook.

I’m involved in evaluating candidates for positions in ML and have been asked how to test their practical skills. We are looking at both potential ML engineers and research scientists. The positions are quite junior, so project management is not something we need to look for. We have a quite broad set of applications going on spanning computer vision, NLP, time series analysis and tabular data.

What I would like to do is to formulate a task (or tasks) that I can use to test the applicants’ practical problem solving abilities. My problem is that the tasks I work with involve a bit sensitive data that I can’t share. Open datasets on the other hand are often already formatted in a “ready-to-model” format with plenty of publicly available solutions, which is usually not the case in real-life projects.

I would like to discuss options that can also serve as a resource for struggling research scientists involved in recruiting. How would you formulate a task that:

• Is solvable within reasonable time before a job interview
• Is not more hardware demanding than that you can solve it in a Collab notebook
• Is not trivially solvable by reading online tutorials
• Shows that the applicant avoid some common pitfall encountered in practice (data leakage, imbalanced datasets, test set peeking)
• Shows that the applicant actually has some practical know-how?

Thought it was a joke, but it looks real….

https://www.sciencedirect.com/science/article/pii/S0957417419307079