In industry and research workflows today, we greedily acquire, label, and train as much data as possible. While more data usually corresponds with better model performance, this is not always the case. New research in data valuation allows us to target the subsets of our data that would train the best model.

In this article we explore cases where less data is better, and how to identify which data is irrelevant to the machine learning task at hand.

Would love feedback on the article!

My model have MSE, BCE and CTC.

It is just summed, but probably it is not best approach.

If I add one variable as a coefficient. Model will simply make it equal to zero.

I thought to do for something like (1-a)loss1 + aloss2, but these functions have different scales, so the model will simply choose loss with minimal scale.

At this point im out of ideas.

Having had the privilege of compiling a wide range of articles exploring state-of-art machine and deep learning research in 2019 (you can find many of theM, I wanted to take a moment to highlight the ones that I found most interesting. I’ll also share links to their code implementations so that you can try your hands at them.

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https://heartbeat.fritz.ai/2019s-top-machine-and-deep-learning-research-papers-1ec363f29e85

Hi all – Relatively new to ML, but have been doing my homework. I have an application where I am trying to generate an image based on data vectors from a non-optical domain. The mapping / relationship is unknown, but let’s assume there is some deterministic relationship. (For example, if I had ultrasonic data reflecting off a target and I wanted to generate an image of the target.) Could I use a cGAN model and train it with known reflection / image pairs? The thing I find confusing is that most if not all cGAN example use a noise vector as the input to the generator. Couldn’t I simply use my non-image (reflection) data as the input vector instead? My simplistic understanding is that the noise vector acts as a “recipe” for the unknown image, and the cGAN is learning to read the recipe through trial and error reduction.

What else should I be diving into to get this working?

Where can I find an IEEE template or example for a software package?

As per this patent application, it seems as though inventors at Baidu are trying to patent spectrogram to waveform synthesis via the use of a convolutional neural network configuration.

If this goes through, it seem as though it would have a huge impact on most of the audio research field when it comes to resynthesizing audio spectrograms.

Am I being alarmed for nothing or is this really bad news?

Are there techniques out there for making NNs attend to certain parts of the input (say, regions in an image), but not to others?
I can imagine replacing the “unwanted” regions with noise or constant values, but are there better approaches?

Thanks for your insights!

Hi all,

I analysed paintings of artists based on their histograms, and then getting a latent representation of their paintings via auto-encoder, when I used t-sne on their latent space expecting to see some wonderful clusters, I got a linear line I don’t know how?

Have a look at it here::

https://github.com/iamnotahumanbecauseiamabot/artwork_analysis/blob/master/art_work.ipynb

Hello. I’m currently working on a financial machine learning project. I was recently reading a paper titled Statistical Arbitrage Pairs Trading Strategies – Review and Outlook (Krauss, 2017 Journal of Economic Surveys) in order to better understand how pairs trading works and how I would be able to apply machine learning to it. I was a bit disappointed with the lack of detail regarding this section in the paper, but this made me thinking: What kind of actual strategies are companies that use machine learning for pairs trading use?

I know that there’s not a lot of information regarding actual trading strategies utilized by companies and individuals, but this is why I was prompted to make a post here in hopes that anyone who may be knowledgeable on the subject would be kind enough to provide their input.

Are there are well-known machine learning strategies that I should be aware of? No need to mention the specific entity name, the name of the strategy would suffice.

Thanks in advance!