Hello everyone!

I found an algorithm that allows me to solve the problem of the optimal partition quite(very) quickly. For any type of score (gini index of the target for example) I can find the partition of the input that minimizes it (even on really big datasets). I remind you that in the case of decision trees this optimal partition is not always reached because the algorithm is greedy.

Since I’m not a data-scientist, but rather a combinatorial optimization specialist, I’d like to know if it’s really a discovery or it’s already been done, for example in decision trees implementations.

I’ve been interviewing for a few months now with various companies. I have a PhD in ML and 5 years of experience in academia and industry, and been actively publishing in prestigious ML and CV conferences and journals for the past 5 years.

During my very frustrating job hunt, I managed to get interviews with 20+ companies, startups, and academic/industrial collaborative institutions. Apart from a couple of the interviews that I quickly discovered wouldn’t be a good fit for my future, all the interviews went really well, and I communicated well with the recruiting management and the recruiters. I travelled across country, paid for accommodation, paid for interview attire (we wear T-shirts to work), got unpaid leave for many days from my current employer, and as such lost lots of money out of my pocket to get a job; not to mention the many hours I had to spend preparing for the interviews, showing up to the interviews, waiting for the recruiter or hiring committee to not show up and then reschedule the whole thing, etc.

I also spent priceless time doing coding interviews or more time-consuming take home challenges to solve. I paid out of my pocket for Google cloud to do the training for the coding challenges. More often than not I felt me doing the interview is actually offering the company a “FREE CONSULTATION GIG”, rather than them wanting to know more about my background, my resume, and gauge my experience and abilities, and see if I’d be a good cultural fit for their team.

All that to hear first-hand from the hiring manager or my connections (friends who work in the said company) that internal candidate got prioritized, or they can’t offer me work permit sponsorship, or the hiring manager wasn’t too sure if they really want to hire someone at the moment, or get hit by a country-wide hiring freeze because of elections (Canada).

My search still continues for the right job, but I’ve been thinking of starting to send bills to companies that outright waste candidates’ time and money, and don’t offer any compensation for accommodation and travel.

What do you think? Are we selling ourselves too cheaply these days or is this a norm and I should get used to getting treated like this when I’m interviewing?

https://en.yna.co.kr/view/AEN20191127004800315

Announced today in South Korea, and it’s made me think on the sort of impact that these things will have on people in the coming days. There’s definitely a great deal of good that can be achieved, with innovation/growth and so many opportunities in general for the companies and people involved in this work.

But at the same time, it is kind of sad to see some of the human element get left behind. I’m sure Lee Se-dol could have played for many more years if he wanted to, continuing to contribute greatly to the professional Go scene as a player.

This is something that I wonder then, if people working at companies like Google / DeepMind should be thinking about. I’m sure the growing profit margins and money that’s flowing in from all our work is more than satisfactory for the company leadership / investors to not have any issues with all this. As the engineers responsible for actually building everything though, is their any kind of ethical consideration on our parts that we need to recognize? I don’t know myself to be honest. But I am curious as to what you all think here in the r/machinelearning community.

Arxiv: https://arxiv.org/abs/1911.11763

Abstract: This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are estimated by solving a differentiable optimal transport problem, whose costs are predicted by a graph neural network. We introduce a flexible context aggregation mechanism based on attention, enabling SuperGlue to reason about the underlying 3D scene and feature assignments jointly. Compared to traditional, hand-designed heuristics, our technique learns priors over geometric transformations and regularities of the 3D world through end-to-end training from image pairs. SuperGlue outperforms other learned approaches and achieves state-of-the-art results on the task of pose estimation in challenging real-world indoor and outdoor environments. The proposed method performs matching in real-time on a modern GPU and can be readily integrated into modern SfM or SLAM systems.

When do non-linear versions of algorithms such as SVMs or neural networks outperform linear methods at a statistically significant level? We researched this question by running experiments on 299 data sets in OpenML. Results show that only in around 20 per cent of cases non linear results are better at a significant level. We also investigated this question deeper by looking at for what type of data sets this happens by looking at number of instances, features and building meta learning models.

Reference:

Benjamin Strang, Peter van der Putten, Jan N. van Rijn and Frank Hutter. Don’t Rule Out Simple Models Prematurely: a Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML. In: Seventeenth International Symposium on Intelligent Data Analysis (IDA), 2018

If I created a model with a bad accuracy, sensitivity etc., how much value can you give the variable importance? Is it nonsense, since he model is bad or does it tell you stuff based on which you can improve?

One of THE Best Papers that I’ve ever read (Both in terms of the research and the paper-writeup itself):

https://arxiv.org/pdf/1911.11423.pdf

The leading approaches in language modeling are all obsessed with TV shows of my youth – namely Transformers and Sesame Street. Transformers this, Transformers that, and over here a bonfire worth of GPU-TPU-neuromorphic wafer scale silicon. We opt for the lazy path of old and proven techniques with a fancy crypto inspired acronym: the Single Headed Attention RNN (SHA-RNN). The author’s lone goal is to show that the entire field might have evolved a different direction if we had instead been obsessed with a slightly different acronym and slightly different result. We take a previously strong language model based only on boring LSTMs and get it to within a stone’s throw of a stone’s throw of state-of-the-art byte level language model results on enwik8. We also achieve state-of-the-art on WikiText-103 – or do we? This work has undergone no intensive hyperparameter optimization and lived entirely on a commodity desktop machine that made the author’s small studio apartment far too warm in the midst of a San Franciscan summer. The final results are achievable in plus or minus 24 hours on a single GPU as the author is impatient. The attention mechanism is also readily extended to large contexts and requires minimal computation. Take that Sesame Street.

In general is this true?

small batch size + long training ~ large batch size + short training 

Just wondering because I’m not aware of any standard literature on this topic, and if anyone knows any good papers I would appreciate some references!

Hi! I was wondering if there have been any models / datasets that have been collected for creating a ML Model (RNN or some other architecture) that would be able to detect from text whether or not a specific text is propoganda from country A, B, or C.

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In recent years, a lot of governments have employed people to make thousands of accounts and thousands of bots to disseminate misinformation. Are there any research papers or datasets that would be useful in this task?

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Thanks!