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[D] How to fuse image and another (additional) structured data(age, gender, etc) in CNN

It's close to solve multi-modal problems.

When using CNN, I want to utilize additional information (i.e. clinical data in medical domain(age, sex, BMI, etc) or any other metadata).

I found one that concat additional features to the output of last fc layer.(then retrain using MLP or SVM). I think there are further fancier methods, but i couldn't find..

Can i get some ideas, keywords or papers?

submitted by /u/visionNoob_r
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Source: Reddit Machine Learning

[D] [Meta] Introducing flairs

Flairs have been suggested at various points, like here: https://www.reddit.com/r/MachineLearning/comments/5dlt6x/d_allowing_flairs_for_users/ or here: https://www.reddit.com/r/MachineLearning/comments/3u6svw/can_we_open_up_flair_selection/

A benefit of flairs is to provide commenters with context when discussing topics with other users.

A detriment of flairs is to introduce "bragging contests".

One aspect of flairs that's somewhat positive and somewhat negative is to figure out how reliable a comment/author is.

Overall, the mod team thinks it's worth trying out. We'll be monitoring how it affects discussions and potentially revert this decision.

These are the 5 different flairs, as well as the intended criteria

  1. Researcher: Has published in machine learning.
  2. ML Engineer: Is working in industry in a non-research role using machine learning
  3. Student: Is primarily learning about machine learning
  4. PhD: Is currently a PhD/has received a PhD in machine learning.
  5. Professor: Is a professor in machine learning.

If you fall into multiple roles, choose whichever one you'd like.

For now, the flair does not need to be verified, unlike r/askscience or r/askhistorians. This is simply to reduce moderator load and reduce friction. However, if we suspect this is a problem, we are open to instituting some kind of verification scheme.

If anyone has arguments for how the system should be changed, or would just like to weigh in on this decision, we're happy to hear it ๐Ÿ™‚

submitted by /u/programmerChilli
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Source: Reddit Machine Learning

Single-batch inference vs Multi-batch inference

Good day fellows,

Im currently writing a paper about a machine learning based topic.

I've stumbled about the multi-batch inference and single-batch inference, where as single-batch inference is run mostly on edge device and multi batch inference on HPC server plants.

Due to my limited language capabilities and lacking google results, I couldnt figure out where the difference between those applications is.

If someone could explain it to me, I would be very thankful. ๐Ÿ™‚

Greeting to you all

submitted by /u/Philipp187
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Source: Reddit Data Science

I have a challenge!

Hello fellow Data Scientists,

I'm gonna ask broad questions to try to gather as much feedback as I can and then decide what to do. Everything is a possibility at this stage, there are no right or wrong. I have some data background but it's not relevant and that's why I'm a bit stuck with my thoughts.

Objective: Create kind of an Hackaton/program with a company where at the end of the day Data is transformed into relevant business insights. Those insights will be capitalized to generate possible new business models.

Some questions about:


– Should we have several teams working with data? how many people per team, 1 or more? how can they work together – can we assign several roles like: 1 to cross data, 1 to clean the data, etc (is this stupid? lol) ?

– Are these people data scientists only? Should we have 1 business guy per team to give a different perspective?

– Should we have Data Scientists in the morning and then business people during the evening?


– Should we start with raw data?

– All the teams should start with the same Data?

– Should we try to cross data from company with some public government data or other sources?


– A morning, 12h, 24h, 2 days? How much time do we need?

– Should we specify some avenues to explore or let them search for everything?

– What are the things I have to take into account?

I Know this is all too broad, vague, open and your answer will be "It all depends on what you want", but what I need here is your expertise and sensitivity. If it was you, what would you do, how and why? Do you have anything that had been done before as a good example?

Thank you so much!

submitted by /u/gonrodrigues
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Source: Reddit Data Science

[D] Paper review call — adding more symmetries and manifold inductive priors to CNNs (Cohen et al)


Convolutional neural networks were revolutionary because they added translation equivariance. Images have "translational symmetries" where the same thing can appear in multiple places. CNNs learn these symmetries with fewer parameters, leading to better generalisation and needing less data to train. But what other symmetries are there? CNNs currently to need to see many examples of augmentations like rotations to learn to recognise objects in the same way because CNNs are not rotationally equivariant. This statistical inefficiency ("hunger for data") is perhaps the most significant practical limitation of current deep learning technology. The other problem is that CNNs are mostly used for data on the planar manifold (i.e. 2d surface), so how do we create versions which would work on other manifolds? Join Ilia Karmanov from Qualcomm Research on a deep โ€œtopicโ€ dive through several papers on this topic. You read it right folks, this is a bit of an experimental departure in style but we will try to cover the contributions from several papers in one call!

Some of this research is by Taco Cohen and Prof Max Welling (Qualcomm Research)


Gauge Equivariant Convolutional Networks

Steerable CNNs

Group Equivariant Convolutional Networks

Deep Spherical CNNs

Gauge Equivariant Convolutional Networks and the Icosahedral CNN

A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations

Why do deep convolutional networks generalize so poorly to small image transformations?

Making Convolutional Networks Shift-Invariant Again

3D Steerable CNNs

Spherical CNNs Harmonic networks: Deep translation and rotation equivariance

submitted by /u/timscarfe
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Source: Reddit Machine Learning

[R] Building a Cross-Disciplinary Network to Tackle Climate Change with Machine Learning

I listened to an interesting talk at the American Meteorological Society about ML + climate change research. Note that this is high-level and somewhat meta, discussing barriers in the ML and climate science fields and defining problems that could use ML.

Kelly Kochanski's recorded presentation: https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/369878

Associated paper: https://arxiv.org/abs/1906.05433

submitted by /u/turing_machines
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Source: Reddit Machine Learning

Modeling the Economic Cost of COVID-19 Under Various Scenarios (Concept) – Your Thoughts

Hey guys,

Just read this article about how there may be more targeted methods to mitigate COVID-19 than a blanket shutdown of society. Maybe re-open businesses to people less than 50 and no heightened risk to the disease once testing is up and running. https://www.nytimes.com/2020/03/20/opinion/coronavirus-pandemic-social-distancing.html

I thought it could be something interesting to design an economic model of – how different approaches could play out in the larger economy. There are a lot of posts right now telling data scientists to stay in their line right now and leave it to the experts, though, so maybe Iโ€™m speaking out of turn. Thoughts?

submitted by /u/i_am_baldilocks
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Source: Reddit Data Science

Portfolio Project Presentation

This is kind of similar to u/Busy-Chipmunk 's recent post.

I am currently creating my website(using Github Pages) for my portfolio and I am curious how I should present my projects. My idea is to have separate pages for each project where the "final product" is shown at the top with some description and then layout the process I took for the project on the rest of the page.

Is this a good idea? Should I present only the final project itself? Maybe link to another page which shows the process in a blog style?

Any suggestions are helpful, thank you.

submitted by /u/CarmelotheOG
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Source: Reddit Data Science

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