Hi all,

I just wanted to get a straw poll opinion on this.

I’m currently a C-suite level employee of a company and I make over $100k/year. My background is backend and front end development and now my job is largely project management with some coding.

I’ve been taking the Fast AI course and wondered whether career transitions to machine learning are quite do-able?

I currently work from home (remote) so would be looking to do the same with machine learning.

I suppose I’m wondering whether salary + remote work would remain as reasonable expectations in a career switch.

Any info or insight would be great 👍

Not sure how many Ultrasound or medical imaging folks are in here, but thought this might be useful to this group. I’m part of an ultrasound research lab at Duke University, and we’ve recently open-sourced work on ultrasound image post-processing which allows one to mimic proprietary post-processing black-boxes found on commercial ultrasound scanners. Here is the: Paper, Github, Colab notebook.

https://arxiv.org/abs/1908.05782

When creating an ultrasound image from scratch, it is common to have speckle noise, Gaussian noise, clutter, reverberation, and other undesirable forms of image degradation. While raw ultrasound images are very familiar to researchers, medical providers will typically only look at heavily post-processed images in the clinic. Unfortunately, commercial post-processing is generally proprietary and kept secret. The inaccessibility makes apples-to-apples comparisons of novel methods to current clinical practice difficult. It also makes the translation of novel methods into the clinic difficult. Ideally, the post-processing is not secret, and everyone can always have lovely images to look at as a baseline. We find that it is possible to mimic the post-processing found on commercial scanners through CycleGANs by just using images acquired via regular use. CycleGANs do not require any image registration or image pairing to train, which is very convenient. We are releasing the fully trained models so that any researcher has access to clinical-grade like post-processing. We refer to our trained models as MimickNet.

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TLDR: Clinical Ultrasound Post-Processing is kept proprietary and secret. However, by using data collected just via intended ultrasound scanner use, it is possible to mimic the post-processing algorithm found on some of the best ultrasound scanners. We are making these models available to any researcher, so we all have access to clinical-grade post-processing.

It all depends on the available data and the variety of activities. You can’t just pour a bunch of data into an ML model and expect it do detect threats. In many cases, the key is to combine human analysts with ML algorithms.

https://bdtechtalks.com/2019/07/29/machine-learning-ai-cybersecurity/

TL;DR: Penalizing wrong explanations increases predictive accuracy for neural networks!

Paper

Code

Abstract: For an explanation of a deep learning model to be effective, it must provide both insight into a model and suggest a corresponding action in order to achieve some objective. Too often, the litany of proposed explainable deep learning methods stop at the first step, providing practitioners with insight into a model, but no way to act on it. In this paper, we propose contextual decomposition explanation penalization (CDEP), a method which enables practitioners to leverage existing explanation methods in order to increase the predictive accuracy of deep learning models. In particular, when shown that a model has incorrectly assigned importance to some features, CDEP enables practitioners to correct these errors by directly regularizing the provided explanations. Using explanations provided by contextual decomposition (CD) (Murdoch et al., 2018), we demonstrate the ability of our method to increase performance on an array of toy and real datasets.

Good afternoon ML community,

I am approaching this problem from a supervised machine learning perspective since that is where the majority of my experience is — so I need a sanity check on if this approach is correct or if I should be using a different approach altogether.

Lets say I have data on approximately 600 apartment complexes, each with about 50-100 features (‘amenities’). These include ‘pool’, or ‘no pool’, ‘pets allowed’, ‘no pets allowed’, ‘small pets allowed’, “more expensive”, “less expensive”,etc.

I also have, for about 15 of these complexes, choice data on rental losses. So– for these 15, everytime somebody chose another complex, they were surveyed and revealed which alternative they chose. There’s about 100 ‘lost choices’ for each of the 15 complexes. My goal is to construct the data in such a way that I can do feature selection on the amenities to figure out which ones play most prominently into choosing another complex, to help understand how to improve the initial 15 complexes.

The approach I was thinking about implementing was constructing a dataset based of differentials and similarities. So for each ‘choice’, there becomes two datapoints: one where we have a list of amenities in complex A vs complex B, and then a counterpoint for the opposite. So it would look like this:

For each datapoint, in the case when complex B is chosen, which we’ll label with an output of “1” for “chosen”, the input data vector would be a list of 0-3 for every amenity in the matrix:

B has this amenity but A doesn't: 0

A has this amenity but B doesn't: 1

Both facilities have this amenity: 2

Neither facilities have this amenity: 3

Then we would create the complimentary data point, where the A and B vector differentials are switched (A has this amenity but B doesn’t: 1, etc) and the output label would be 0 for “not chosen”.

Logically this makes sense to me, but I can’t help but think I am over complicating it– and I can’t think of any other way to instrument the data. Once it’s instrumented in this way, I could either build a classifier (xgboost) and look at feature importance of all the choices of ‘1’, or do feature selection analysis on the data to come up with which features to focus on. Does this seem like a good approach, or are there some glaringly obvious drawbacks and/or better tools for this?

This work provides the first unifying theoretical framework for node embeddings and structural graph representations, bridging methods like matrix factorization and graph neural networks. Using invariant theory, we show that the relationship between structural representations and node embeddings is analogous to that of a distribution and its samples. We prove that all tasks that can be performed by node embeddings can also be performed by structural representations and vice-versa. We also show that the concept of transductive and inductive learning is unrelated to node embeddings and graph representations, clearing another source of confusion in the literature. Finally, we introduce new practical guidelines to generating and using node embeddings, which fixes significant shortcomings of standard operating procedures used today.

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https://arxiv.org/abs/1910.00452

Until recently, recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have been used to tackle this challenge. The problem with these is that they aren’t able to keep up with context and content when sentences are too long. This limitation has been solved by paying attention to the word that is currently being operated on. This guide will focus on how this problem can be addressed by Transformers with the help of deep learning.

https://heartbeat.fritz.ai/research-guide-for-transformers-3ff751493222

This seems to be the trend in ML/Vision/NLP