I’m going to a 2 day machine learning hackathon. 2 days isn’t a lot of time to train a brand new model, so I’m hoping to make something that would assist other ML people. 2 days will probably just be enough time for a prototype with core functionality.

If it makes a difference, I’ll be working in Pytorch.

I’m at ACL this week and some people give way better conference talks than others. Professors and keynote speakers I get, they have had a lot of practice. But how do people who are PhD students get so good at giving talks? e.g. Sebastien Mielke or Angela Fan

Reddit, What are your tips for me?

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Hi all! I graduated with a BS in computer science last spring and have spent the last few months building and deploying my first large-scale ML project. I would love to hear any feedback or criticism you have to offer. Thanks!

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I was wondering if anyone has experience using transformer architectures for time series forecasting? Did it work well or if it didn’t why not? In particular has anyone used Transformer-XL? Just intuitively I was thinking it could work well for handling really long term dependencies in time series data. However, I haven’t seen any recent research mentioning it being used outside of NLP.

I’m working on a project in digital signal processing (DSP). To put it simply, I’m trying to create a model that can mimic effects applied to a musical signal. For instance, the domain, X, is clean guitar signal and the range, f(X), is the same guitar signal under some effect, f. This function f could be distortion, chorus, delay, reverb, etc…. All that matters is that f maps a clean guitar signal to some altered signal f: X -> f(X).

My modeling task is to model f without know what the function, f, is exactly. I’ve successfully trained a LSTM model to mimic the effect of chorus and I’m sure I could train a model to model other effects such as delay or reverb.

My issue is that the predictions on new signals take so long. A couple seconds of audio can end up taking minutes to predict. I currently make predictions on a sample-by-sample level (an average sampling rate is 22,000 per second). I’m trying to find a solution that could make predictions (i.e. alter the input signal) in near real-time. Is there a specific type of modeling I can try that will results in a model that can make fast predictions? Or do you have any ideas on how to take a model and allow it to make near real-time predictions? The thing is that in reality these affects can be applied so quickly because they are simple transformations. For instance, chorus is just a result of phase-shifting a signal and adding it back to the original signal. Any help is appreciated. Thanks!

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I was looking for state of the art works on action recognition. What are some of best works of recent times?