Hype | MachLearning

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2021-07-25

COVID-19 Vaccine Rates By U.S. County?[reddit]/r/datasets

Has anyone come across a dataset of vaccine rates by county in the US? I have checked Johns Hopkins Git and don't see it in there.

[share]4	github 13 Get as Jupyter Notebook @a2c.fyi	2021-07-25
2021-07-22: Accelerating Quadratic Optimization with Reinforcement Learning https://arxiv.org/abs/2107.10847v1In future work, we will explore whether additional RL policy options can speed up convergence rate further, such as training a hierarchical policy [2] in which the higherlevel policy determines the interval between adaptation, performing a neural-architecture search [11], using meta-learning [13, 39] to speed up problem-specific training, and onlinelearning to adjust the policy at runtime to adapt to changing problems. ‹› First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapidly solved. These methods face two persistent challenges: manual hyperparameter tuning and convergence time to high-accuracy solutions. To address these, we explore how Reinforcement Learning (RL) can learn a policy to tune parameters to accelerate convergence. In experiments with well-known QP benchmarks we find that our RL policy, RLQP, significantly outperforms state-of-the-art QP solvers by up to 3x. RLQP generalizes surprisingly well to previously unseen problems with varying dimension and structure from different applications, including the QPLIB, Netlib LP and Maros-Meszaros problems. Code for RLQP is available at https://github.com/berkeleyautomation/rlqp.

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2021-07-25

[P] Am I losing out using google colab?[reddit]/r/MachineLearning

I am approaching the stage where I'm trying to tweak parameters and improve a model that seems to be working in google colab. It's an inpainter using a pretrained StyleGAN and to inpaint it takes about 4 mins per image.

Throughout the project I've had SSH access to a GPU (specs: https://www.maths.cam.ac.uk/computing/faculty-hpc-system-fawcett, and about 1/4 of the time there is an annoying queue), though have found myself repeatedly coming back to google colab.

My question is: am I losing out? I've loved the quick experimentation nature of colab, and even with the VSCode SSH extension I find the file transport stuff much smoother in colab (without sudo access on the server, and still being a terminal n00b to some extent). But it seems that colab is mostly recommended for very 'toy' experiments, and I'd like to think a project that has potential to beat benchmarks isn't a 'toy'!

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100

2021-07-25

[D] What does your cloud/docker workflow look like? How do you get anything done with these tools?[reddit]/r/MachineLearning

I feel like I'm going a little crazy so I wanted to ask this sub as it seems like there are a fair number of people here in relatively mature ML environments. I used to work at a company that had a fairly old-school development workflow (as in, modern in 2015). Individual DS/ML researchers would have fairly beefy personal machines (32+GB of RAM, big CPU, xx80 or two if necessary). We were not doing any groundbreaking DL research or anything but working on training data of usually a few GB. We used docker but it was more of an engineering thing than anything I dealt with day to day. Personally I never had trouble with just sharing environment files. We would use some shared HPC resources but again, it wasn't anything too sophisticated (and imo we didn't need anything like that).

Now, I work at a company that is supposedly more "modern" using the cloud and docker-first (similar scale of data and problem type), but the I find the workflow soooo slow and hard to deal with. Here, we do all the development inside cloud docker containers themselves. But it seems like there are 25 micro-steps to even get started on a project and so much overhead for such little gain. Okay let me spin up instance, ssh, pull docker image, mount shared drive, forward port, git clone, etc etc etc. Can't use PyCharm or even VS Code since the dev environment is inside a docker on a remote machine (I know you can technically do this with VS Code, but it's finnicky imo). Every day I am dealing with micro-problems arising from this. And at the end, when it comes time to deploy, I still need to ping MLOps guys to do anything useful with the image. I feel like I spend half my day on this and half my day on the actual problem at hand. What exactly is the point of all this? Are we even saving money on all these cloud instances being spun up and down?

Anyway, I asked my boss about this, and he doesn't see a problem since everyone else on the team is a linux ninja and just has a million little aliases and copy/paste cli routines to do things, and knows all the gotchas and they get work done, so why is it a problem? Why am I a special snowflake who NEEDS to use modern IDE like PyCharm rather than being happy with a simple notebook? Does everyone on modern ML teams work like this? Does this org need to hire more MLOps/Engineers or get one of these fancy cloud solutions? Seems a lot more expensive than just specing out a beefy desktop machine where everything is dead simple and easy to manage. This company is not Uber or Facebook, we are not working at global scale or preparing for hyper-growth.

/Rant!

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2021-07-24

[D] Principal Component Analysis(PCA)[reddit]/r/MachineLearning

I have only used it in small personal datasets such as iris and diabetes. Folks who are working on ML models, is there an actual real world application usage of PCA other than hiding confidential user data? Does it really improve the output label classification? If yes which of the PCA type do you use and how do you determine when to use it.

[share]19	@MelMitchell1	2021-07-24
@anthlittle[complexityexplorer.org]

[share]44	@MelMitchell1	2021-07-24
@judell Main take: robotics is hard! It takes more than just training on lots of data. https://twitter.com/MelMitchell1/status/1416522005709344768

[share]114	@MelMitchell1	2021-07-24
If you're at the #ICML2021 workshop on Adversarial Machine Learning today, come see our poster about how a small degree of adversarial robustness can dramatically increase the transferability of targeted adversarial examples. [openreview.net][github.io]

[share]60	@ylecun	2021-07-24
Un article dans Le Monde par David Larousserie sur l'mergence de l'apprentissage auto-supervis pour la vision par ordinateur. [lemonde.fr]

[share]778	[ ImperialCLondon ]Get as Jupyter Notebook @a2c.fyi	2021-07-24
2021-03-23: iMAP: Implicit Mapping and Positioning in Real-Time https://arxiv.org/abs/2103.12352v1We pose dense SLAM as real-time continual learning and show that an MLP can be trained from scratch as the only scene representation in a live system, thus enabling an RGB-D camera to construct and track against a complete and accurate volumetric model of room-scale scenes ‹› We show for the first time that a multilayer perceptron (MLP) can serve as the only scene representation in a real-time SLAM system for a handheld RGB-D camera. Our network is trained in live operation without prior data, building a dense, scene-specific implicit 3D model of occupancy and colour which is also immediately used for tracking. Achieving real-time SLAM via continual training of a neural network against a live image stream requires significant innovation. Our iMAP algorithm uses a keyframe structure and multi-processing computation flow, with dynamic information-guided pixel sampling for speed, with tracking at 10 Hz and global map updating at 2 Hz. The advantages of an implicit MLP over standard dense SLAM techniques include efficient geometry representation with automatic detail control and smooth, plausible filling-in of unobserved regions such as the back surfaces of objects.