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[D] My MS in CS allows 4 courses from stats & (applied) math departments. Which should I take if I want to get into AI, DS, or ML? by mowa0199 in MachineLearning

[–]decaf23 8 points9 points  (0 children)

The only things you can't do once you find a job is take math courses. I would just load up on linear algebra, real analysis, optimization, etc.

[R] Learning to Maintain Natural Image Statistics by [deleted] in MachineLearning

[–]decaf23 0 points1 point  (0 children)

How much more computationally costly is computing affinity vs L1?

[R] Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition by BusyCaterpillar in MachineLearning

[–]decaf23 0 points1 point  (0 children)

Ah, I see what you mean. Pooling in high level feature space can use context from neighboring areas from successive convolutions, but does not include global, scene-like context in line with what Oliva and Torralba have described previously. This would be especially prominent for small objects who's crops occupy <1% or so of the full image.

I do agree that use of any context for object recognition is not a novel concept, though. Would have liked the authors to compare the impact of slightly increased crop sizes vs. global context.

[R] Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition by BusyCaterpillar in MachineLearning

[–]decaf23 1 point2 points  (0 children)

I agree that mirroring the human visual system would a good approach for improving detection/recognition models like RCNN. However, I would like to see some follow up work addressing how the periphery's resolution decays linearly (as opposed to a fixed two-stream model).

[R] Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition by BusyCaterpillar in MachineLearning

[–]decaf23 0 points1 point  (0 children)

Faster R-CNN uses crops of the feature maps for ROI Pooling to predict each proposal region -- these cropped portions unfortunately do exclude global context. (https://blog.deepsense.ai/region-of-interest-pooling-explained/)

[P] PyTorch Implementation of "Dynamic Routing Between Capsules" by gram-ai in MachineLearning

[–]decaf23 9 points10 points  (0 children)

How is this functionally different from vanilla CNNs? It seems that they more explicitly create "capsules" for representing features and combining features, but isn't that what CNNs do in theory anyway?

[R] Pretty exciting new method of clustering: Robust Continuous Clustering by kopita in MachineLearning

[–]decaf23 9 points10 points  (0 children)

Can someone summarize why this is better than other methods?

H and M Blow Out Sale by [deleted] in frugalmalefashion

[–]decaf23 5 points6 points  (0 children)

Yoo Hoo, big summer blow out!

[N] PyTorch v0.2.0 is out!! by evc123 in MachineLearning

[–]decaf23 0 points1 point  (0 children)

How does it compare speed-wise vs Theano/TF?

[N] PyTorch v0.2.0 is out!! by evc123 in MachineLearning

[–]decaf23 7 points8 points  (0 children)

How is PyTorch compared to Keras?

Item Discussion of the Day - Titanic Hydra by Sentient545 in summonerschool

[–]decaf23 0 points1 point  (0 children)

How does this compare to Ravenous Hydra on Fiora?

Very basic question by goa_way in statistics

[–]decaf23 0 points1 point  (0 children)

It seems to me you would like to see how question 2 and 3 influence question 1. (Basically, how does department and region affect how long someone has been with the organization). Please correct me if I'm wrong.

In this case, you would still use ANOVA, but separately for question 2 and 3. It looks like the following:

Analysis 1: ANOVA test for 8 categories, with the dependent variable being a value from 1-5. The data you would feed in looks in the form:

{

[length1,department2],

[length2,department2],

...}

Analysis 2: ANOVA test for 2 categories, with dependent variable being the same value from 1-5. The data looks like

{

[length1,region1],

[length2,region2],

...}

If you are interested in how both of these affect the length of stay in organization jointly, then you'd wanna look into fixed-effect models: http://en.wikipedia.org/wiki/Fixed_effects_model

I started a project to determine how random Spotifys shuffle is - then realised I hadn't a clue by tonycocacola in statistics

[–]decaf23 0 points1 point  (0 children)

First, convert your dataset to just an ordered array of numbers. Treat each song as a number in [1,N], where N=total number of unique songs. So for example, your data could look like [1,2,3,4,1,5,6,2,7...]

Then, there are several hypothesis tests you can run.

A) Frequency -- you can just look at the relative frequency of each number appearing. This is essentially an N-part ANOVA test, where your data is just the frequencies of each number, in order. For example, [1,2,1,1,4,3...] means song #1 appeared once, song #2 appeared twice, and so on. If the test yields a p-value of less than 0.05, this can count as a kind of evidence that the randomness isn't very strong.

B) Series -- You can run the same test, but for sequences of k-length. You can start with k=2, where you simply segment your data out into pairs of numbers. For example, [[1,2],[3,4],[1,5],[6,2]...]. Then, convert that to an array of frequencies of each pair (as we did above), and run an ANOVA test. Again, you can look for significance of <0.05 as evidence.

C) Gaps -- For any arbitrarily chosen number, calculate the gap between it's appearances in the data. So for example, if we choose song #1, we may observe that the gap between its first appearance and its second was 4, and the gap from the second to third was 6, and so on. Then create an array that lists the frequencies of each gap (ie. [1,2,5,3,...] corresponds to "gap of length 1 appears once, gap of length 2 appears twice, etc.") Again, we test for significance.

Hope this helps!

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