Hi all, I recently had to abandon a couple personal projects due to problems with data and training cost. This got me thinking about whether many others in the community share these issues, of if they were unique to my niche projects. If you’ve spent much time on ML projects lately, I’d appreciate if you could take this 5-10 minute survey. Thanks!!

https://forms.gle/S6LM74gkh5uoMTKK8

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Also, if you’ve run into other problems not addressed in the survey, I’d love to hear about them

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Let me know if this breaks the rules or should go elsewhere

Is there a way to combine both image and non-image while training GANs. So far most papers I’ve read seem to use one or the other but not both.

Hi, I’ve written a post describing the start of the art clustering algorithms. They all use deep learning! On MNIST, some models can get 97%+ accuracy without using any label.

https://towardsdatascience.com/an-overview-of-deep-learning-based-clustering-techniques-ff640f108b5d

Some of the papers/repos I admire the most don’t necessarily have the most important methods but have the nicest tables, layout and cleanest code and clearest APIs.

I know I’m conflating several notions of design here but I’ve noticed that some researchers seem to excel at both writing beautiful papers and code as well as maintaining visually nice and well-written blogs. There seems to be a pattern in the sense that care is afforded to the presentation of the work, and this quality becomes a part of the “brand” of the researcher.

Meanwhile, some of the code that has been the most useful for my research has been appallingly written and some of the papers that I’ve found the most useful have been barely understandable and riddled with grammatical and even spelling mistakes.

Obviously one should strive to present their work in the best way possible, so how does a PhD student who writes average quality research code and creates average Inkscape figures “level up” in terms of research and code presentation?

I have this case study for forecasting the sales for a new mobile phone. I have sales and order quantity per week for two predecessor phones ( 2 years worth of data). I am currently looking at some time series models but was wondering if there were any other machine learning models that would be better to use.

I’ve recently started getting interested in AI. As far as I know, from my friend who is more familiar with AI, in reinforcement learning only 2 concepts are applied: reinforcement (layman’s term: reward), and punishment.

However, in behavioral science there are many more elements that may be useful to implement in an AI. There are concepts such as:

1. Discriminative stimuli, or stimulus generalization.
2. Continuous and discrete stimulus/response fields.
3. “Schedules of reinforcement” which change the frequency of an organisms response to a stimulus (such as variable ratio, fixed ratio, variable interval, fixed interval)
4. etc.

I’m not familiar with any AI that uses these concepts and others from behavior analysis. Is anyone familiar with any AI that tries to implement these?

It seems to me a concept such as stimulus generalization may be a useful step in artificial general intelligence for ‘one shot learning’ – to give an example from a study: if you reinforce a pigeon with food everytime it pecks a key under a 550 milimicron (color wavelength) light, then you stop reinforcement (giving it food if it pecks), and you change the color of the light slightly to 560 or 570, the pigeon still pecks the key but fewer times. Therefore ‘stimulus generalization’ occurs. In other words, the pigeon responds similarly to similar stimuli (but with a reduced frequency). The more you change the color – to say 580, then 590, the less responses (or pecks on the key). This also occurs with punishment.

Is anyone familiar with this being applied to AI? Or anything additional from behavioral science for that matter? If only reinforcement and punishment are applied I fear that’s really limiting the great potential of AI.

I have this phenomenon that I don’t understand.

My loss is the cross entropy of the softmax prediction with one-hot encoded labels. The accuracy is a simple argmax comparison.

While the accuracy stays the same, the loss is heavily increasing. I would guess the reason is that all outputs get more equal but the correct label stays the highest values.

But what could be the reason for such a behavior? Why is that happening? What can I do about it?

They both look like problems of finding the top eigenvectors. What am I missing?