Hi All,

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Looking for some guidance with respect to CT scan analysis. There is currently a similar software called “Dragonfly” (if you search Dragonfly CT it would come up on google) with an integrated deep learning mechanism. For my purposes, I feel like this is overkill. I simply need to define the line between bone and tissue for each individual slice (can be done through mouse directed spline creation or XY coordinates per slice — would probably be harder to code for). My thoughts were to use TensorFlow and integrate that into the current software I use, but I was wondering if anybody here had some better ideas. All scans are presented on an 8bit gray-scale with individual values pre-determined by the software (could be useful to differentiate between tissue and bone).

Thanks-ML

Paper: Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

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The system is able to initialize the parameters of both the generator and the discriminator in a person-specific way, so that training can be based on just a few images and done quickly, despite the need to tune tens of millions of parameters

I wrote a blog post explaining how making the Discriminator output a vector instead of a single value can be useful for some tasks:

https://towardsdatascience.com/a-new-way-to-look-at-gans-7c6b6e6e9737

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Has the idea of Siamese Discriminators in GANs been explored before? I couldn’t find anything on this particular matter.

Learn the fundamental concepts of Machine Learning in this talk with world-recognised expert Stephen Magill, and then delve into how Machine Learning can be used to analyse and improve code.

https://youtu.be/W5wD5mIeKws?list=PLEx5khR4g7PLIxNHQ5Ze0Mz6sAXA8vSPE

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Talk Abstract:

Machine learning is permeating every facet of our lives, from learning our preferences to self-driving cars, but what happens when you apply neural networks to code? How do you even view code as data? The key ideas are easy to summarize and fun to play with. This talk will provide an overview of fundamental concepts of machine learning, and then delve into how learning can be used to analyze and improve code. The talk will also provide pointers to available commercial and open source tools and discuss what’s been achieved so far (coding in English, context-aware code completion, automated Stack Overflow). The talk will close with speculation on where the field is going, and how machine learning won’t take our jobs, but hopefully will take over some of the repetitive work we don’t like doing.

During k-fold cross-validation, is it possible that a model is so sophisticated (e.g., with many hyperparameters to be grid searched) that it gives a good score on almost every validation fold? It’s like the model is intricate enough to somehow leak out to fit the validation set every time (there are k times), essentially overfitting the whole training set (because the sum of the k validation folds is just the whole training set).

If this is possible, then I feel like it’s also possible that this best model will eventually have a high generalization error when tested on the final test set, essentially making cross-validation useless. Did I miss anything here?

Let’s say I complete my PhD in machine learning with 3 first author papers in NIPS/ICML/etc. This is widely considered to be the minimum requirement for graduation in the US. Would I have any chance at research scientist positions in industry labs, or would the only doors open to me be glorified software engineering positions? My understanding is that scientist positions are reserved only for the best and that a minimally productive PhD does not put me in a good position for these jobs. Do any of you have any perspective on this? Thanks in advance