Wookai

Great writeup, always interesting to see people digging into issues like that in such detail. I work at Google and shared the article with the Gboard team. Thanks!

We (Google Research) have a similar work that we recently published: https://blog.research.google/2024/03/screenai-visual-languag...

Google employee here, xmanger is one of the main ML experiment tracking/orchestration tool we use internally, I'm pretty excited that it is now available for other to use!

In a nutshell, xmanager allows you to:

- define an experiment, which is a collection of one or more work units (think combination of hyperparamters)

- manage the different jobs/executable required to run this experiment (TPU workers, tensorboard job, etc.)

- collect and display measurements from work units (loss, other metrics)

- keep a reproducible artifact which allows you to re-run the same experiment at any point in the future

See e.g. https://github.com/deepmind/xmanager/blob/main/examples/ for a few concrete examples of a launcher scripts.

I wish they had included screenshots of the tool itself in the repo, I'll make that suggestion :).

If you are interested in time series predictions, I would suggest you had a look at darts (https://github.com/unit8co/darts). It's a well designed library which provides a unified API to deal with time series and try/compare different algorithms/frameworks like Prophet, recurrent NNs, etc.

Yeah, that's a pretty common problem. I'm sure they are actively working on making sending feedback easier :). In the meantime, your technique worked! The issue is now fixed!

That's pretty weird, thanks for reporting this! I work at Google in the Maps org, I reported your bug internally and will update this once I have a resolution.

In the future, if you encounter a similar situation, consider reporting it (https://support.google.com/maps/answer/6194894). Any user feedback is greatly appreciated!

Awesome project, I love it! If haven't seen it yet, Google has some hand-picked highlights of places with stunning aerial pictures: https://earthview.withgoogle.com/. Some high-level info here: https://www.blog.google/products/earth/most-stunning-images-...

EDIT: I read the readme and of course you mentioned Earth View :). Leaving the links for other people who might be interested.

I would highly recomment David Duvenaud's PhD thesis [1] for anybody interested in Gaussian Processes. I find his chapter [2] on expressing structure with kernels super interesting and quite intuitive. It's also one of the PhD theses with the best-looking graphics I've read!

[1] http://www.cs.toronto.edu/~duvenaud/thesis.pdf

[2] https://raw.githubusercontent.com/duvenaud/phd-thesis/master...

Editor's note here: https://msdn.microsoft.com/en-us/magazine/mt833403

Instead of a tactical exploration of a thorny technical problem or new tool, this article digs into patterns and practices that can be critical to a successful machine learning project. The article delves into four key aspects of machine learning management: Connecting with users to enable closed-loop feedback, applying machine learning to the right objectives, building systems to support machine learning-based solutions, and understanding what goes into running a machine learning-based system.

Thanks all for the great feedback and discussion, I'll update this thread once I push an update. If you're interested, there was a great discussion on /r/MachineLearing as well: https://www.reddit.com/r/MachineLearning/comments/b2oiaj/d_b...

Indeed, it's pretty useful to be able to rasterize only parts of the plot! Glad you find it useful!

Indeed, TikZ is great to create beautiful-looking plots! A friend of mine is quite good at it, e.g. look at Figure 2 in [1] and Figure 2.1 (p. 18) in [2]. For complex figures, though, I find that TikZ can be a bit hard to master and sometimes results in longer compilation times.

[1] http://www.hrzn.ch/publications/tf-icnp15.pdf [2] http://www.hrzn.ch/publications/thesis.pdf

Indeed, they're pretty cool (although in my experience the resulting TikZ code sometimes slows down compilation quite a bit).

I'm sorry ! It has been online for 4 years now, I simply never thought of sharing...

It's not exactly what you want, but you can do parametrized plots in TikZ to plot a given function for some range: http://www.texample.net/tikz/examples/parameterized-plots/.

I wouldn't be surprised if someone wrote a tool that allows you to use a reference to an equation as the function to plot :).

Indeed, I remember having to switch to Agg for this reason but can't remember why I switched back. Maybe some rendering issues I had with Agg, not sure...

That's a great point! Templates are a great tool to generate big tables from results, I usually do that for most of the results in my papers, makes it easier to have the odd copy/paste error. I might add this to the tips and tricks, thanks!

It takes a while to get the "muscle memory" of adding a new line after each sentence but it does make things much easier!

Looks awesome, thanks for sharing!

Indeed, sorry about that!

Oops, I forgot to erase that when shortening the title, sorry! Not sure I can update it now :(...

Well, Sonos is actively working on Alexa integration (actually, integration with any voice-controlled assistant): https://www.cnet.com/news/sonos-demonstrates-amazon-alexa-co...

In the meantime, you can connect an Echo Dot to a Play:5 (or any other Sonos speaker that has a line-in): http://www.sam-mallery.com/2016/12/5-tips-for-using-amazon-e...

Based on this plot, my naive reaction would be to decide that DBSCAN is the best algorithm, as it recovers the (qualitatively) best clusters in each case. Anybody has experience with DBSCAN? What are the downside?

You can get more technical details in the scientific paper: Julien Herzen, Albert Banchs, Vsevolod Shneer and Patrick Thiran. CSMA/CA in Time and Frequency Domains in IEEE ICNP, 2015. http://www.hrzn.ch/publications/tf-icnp15.pdf

Link to technical paper: http://infoscience.epfl.ch/record/186014/files/saw-infocom13...

Robson, I got your email, thanks. I just answered. Cheers!

Sure, many factors can explain the initial "surge" in pledges: pre-launch marketing, featuring on recently launched, novelty, first spread on social medias, etc. I did not really try to explain the causes of the different phases, but merely to use the raw data to predict the state at the end (successful or failed).

I haven't done it yet, but I could. This also depends on what your goal is, and how long the campaign last, but it is quite easy to extract such probabilities of success. For instance, this plot shows the estimated probability of success as a function of time and the proportion of the goal that was pledged: http://imgur.com/hoTzBEW. From it, you can see that you need to be at about 25% or more of your goal in the middle for your campaign if you want to have more chances of success than failure. Similar plots could be done by including more factors.

No problem, I'm glad to get feedback, I didn't take it negatively ;).

Thanks a lot for your kind comments!

It is correct: on average, failed projects ask for more money than successful ones, except for projects in the Video Games category. There, it is the opposite. I'll add a "Stats" page to the website as soon as possible to show these results.