I see in some places recommending ensembles of linear models, like xgboost documentation.

Why? Isn’t the ensemble of linear models also a linear model? Is it just a fancy form of regularization/cross-validation, by not using the whole dataset?

There are a lots of real life application on ML nowadays. But the use of rare pattern aren’t discussed much elsewhere.
As the common application, it is used in Market Basket Analysis like frequent pattern mining. But i think there are much more field where rare pattern mining can be used.

I was hopping to work on an unique topic on ML using rare pattern mining.
Hope that i will get some useful suggestions here.

Thanks in advance.

This article will bring you up to speed on some of the most common — and a couple of very recent — techniques for performing automatic speech recognition in a variety of contexts.

https://heartbeat.fritz.ai/a-2019-guide-for-automatic-speech-recognition-f1e1129a141c

The papers/abstracts mentioned and linked to above also contain links to their code implementations. We’d be happy to see the results you obtain after testing them.

Hi There –

My company has a defined role of “Persona Owner” — but this is something I’ve never heard of before and can’t find evidence of it existing elsewhere. So I’m wondering what you’d call it.

The idea is simple — find a senior, experienced individual for each persona we’ve internally identified to come in and take ownership of all aspects of our products and services that are aimed at that particular persona.

For example, if we’re making a boat, we’d have a “Captain” persona owner to oversee all aspects of the boat to make sure that, at the end of the day, it’s a boat that does what they want it to do — something a Captain would buy. In this role, they’d oversee our team of boat makers (carpenters, electronic experts, mechanics, etc) to make sure the boat they make is best in class.

There are some obvious flaws in this example — is the Captain a commercial fisherman or a tour boat operator? Because that would affect the type of boat we build for that person. But assume for a moment that we have enough specificity for each role to get beyond this sort of thing.

I put together a job description, but it’s had limited success, which tells me I’ve described a position that isn’t common, or perhaps something that senior people would rather not be involved in. This role is highly technical, yet not a coding position, and skews towards product management, but is thought of as more of an engineer role. Confusing, I know.

And that’s what brought me here. Would love your feedback. Or if this is an invalid post (or if there’s a better thread please let me know).

Thank you in advance for your time.

Good morning everyone.

I am working on a project where I have a dataset of images regarding an industrial product and I am currently classifying each image into two classes, let’s say [good, bad].

In order to correctly classify the images, I am currently applying the following techniques in this order: HOG + PCA + Random Forest Classifier.

My performance are really good, but I have the necessity to know a specific information, which is roughly the following: “this area of the image weight X% on the final classification”.

First of all, I am executing HOG, so the “area of the image” could be the HOG Cell. Then, I need to gather some feature importances. in order to get this information, Random Forest comes in handy because it will let you know the feature importances in percentage form, once trained. But here the problem arises: my features comes from a PCA. Now, is possible to execute an inverse transformation on a PCA output array and that will give you back the original features array before the application of PCA (which for me is the output of HOG).

In my scenario, will it make sense to apply the inverse transformation to the array of feature importances? And if not, which approach should I go for?

Thanks in advance!

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Edit: added link

I am currently reading the Deep Matching Prior Network paper. The suggested network is used to get better predictions on scene text. Instead of using rectangular bounding boxes, they apply quadrangle shapes to get better localisations of text.

I wondered if this could be applied in Object Detection algorithms, such as faster R-CNN.

Could anyone discuss in more detail why, or why not, this could be implemented and if so what the possible outcome might be?

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Paper source: https://arxiv.org/abs/1703.01425

As there have been some interesting discussions on the alternatives to backpropagation lately (e.g. this reddit thread), I am sharing our latest work just made available on arXiv:

Learning without feedback: Direct random target projection as a feedback-alignment algorithm with layerwise feedforward training

arXiv link – PyTorch code

Summary: Building on feedback-alignment algorithms, we show how to train multi-layer neural networks using random projections of the target vector, which enables layerwise weight updates using only local and feedforward information. The proposed algorithm is called direct random target projection (DRTP). While backpropagation (BP) requires forward and backward weight symmetry (i.e. weight transport problem) and implies update locking before forward and backward passes have been completed, DRTP solves both problems toward higher biological plausibility and low-cost hardware implementation. Indeed, estimating the layerwise loss gradients only requires a label-dependent random vector selection, making adaptive smart sensors and edge computing the ideal applications due to limited power and computing resources. Despite its simplicity, we demonstrate on the MNIST and CIFAR-10 datasets that DRTP performs close to BP, feedback alignment (FA), direct feedback alignment (DFA) algorithms.

The PyTorch code (link above) also includes implementations of FA and DFA.

Feedback is welcome!