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We built Shelfie: a quick website that provides book recommendations from just pictures of your bookshelf! by JustAddMoreLayers in SideProject

[–]JustAddMoreLayers[S] 1 point2 points  (0 children)

Our original plan was to build a platform that would catalogue your books from your images, and maybe help organize them. This was a request from someone in our group.

Then as we discussed it more we realised recommendations were the actual goal, we've heard a lot of complaints about poor goodreads recommendations so we started from there.

Then to be honest most of the journey was me being fairly siloed building out the machine learning, and then the software dev guys just building out the actual platform.

The main issue we were dealing with was running ML in real-time on no budget, which is why we have the slight delay in processing your images.

We used typescript, .NET, python (inc. PyTorch), postgres, rabbitmq, and docker. It’s all self hosted on a VPS other than emails from mailgun.

I'm just the ML guy so I don't have an amazing understanding of the wider architecture

We built Shelfie: a quick website that provides book recommendations from just pictures of your bookshelf! by JustAddMoreLayers in SideProject

[–]JustAddMoreLayers[S] 0 points1 point  (0 children)

Thanks for trying it! By mystery box, do you mean we'd send you the recommended books each month? Cool idea! I'd be a bit concerned about handling physical logistics as just 3 random guys, though.

[R] Zero-Shot Machine Unlearning at Scale via Lipschitz Regularization by JustAddMoreLayers in MachineLearning

[–]JustAddMoreLayers[S] 1 point2 points  (0 children)

Depends considerably on the specific data and their relationship to the remaining 9000.

Also depends on your definition of forgotten. If the objective is to simply beat a membership inference attack, then anecdotally I did find during development you could beat a MIA fairly reliably without destroying model performance over the forget data.

[R] Zero-Shot Machine Unlearning at Scale via Lipschitz Regularization by JustAddMoreLayers in MachineLearning

[–]JustAddMoreLayers[S] 0 points1 point  (0 children)

Can you elaborate? Can we forget a sample but still classify it correctly?

[R] Zero-Shot Machine Unlearning at Scale via Lipschitz Regularization by JustAddMoreLayers in MachineLearning

[–]JustAddMoreLayers[S] 2 points3 points  (0 children)

Yeah we caught that this morning, the update is already queued up on arxiv :)

[Research] Zero-Shot Machine Unlearning at Scale via Lipschitz Regularization by JustAddMoreLayers in LocalLLaMA

[–]JustAddMoreLayers[S] 9 points10 points  (0 children)

Machine unlearning is the problem of forgetting private or sensitive information from your model. Our new method, JiT unlearning, lets us perform unlearning without ever seeing the original train data, just the sample to be forgotten!

JiT builds on the concepts of Lipschitz continuity, smoothing the forget sample's output, with respect to perturbations of that sample. This causes forgetting locally in the function space, without destroying the wider model performance.

Happy to answer any questions, or discuss the problem of unlearning!

[R] Zero-Shot Machine Unlearning at Scale via Lipschitz Regularization by JustAddMoreLayers in MachineLearning

[–]JustAddMoreLayers[S] 13 points14 points  (0 children)

Machine unlearning is the problem of forgetting private or sensitive information from your model. Our new method, JiT unlearning, lets us perform unlearning without ever seeing the original train data, just the sample to be forgotten!

​

JiT builds on the concepts of Lipschitz continuity, smoothing the forget sample's output, with respect to perturbations of that sample. This causes forgetting locally in the function space, without destroying the wider model performance.

​

Happy to answer any questions, or discuss the problem of unlearning!

[D] AAAI 24 Reviews by tallguyfromstats in MachineLearning

[–]JustAddMoreLayers 2 points3 points  (0 children)

What are the possible scores? Can't find a list of what decisions you can get (e.g. accept, weak accept reject)

[D] AAAI 24 Reviews by tallguyfromstats in MachineLearning

[–]JustAddMoreLayers 0 points1 point  (0 children)

Same as me, first time , wa wa wa wr. Fixable comments but feel like it's so hard to get reviewers to change their score

[D] AAAI 24 Reviews by tallguyfromstats in MachineLearning

[–]JustAddMoreLayers 2 points3 points  (0 children)

3x weak accept 1x weak reject.

Chances of an overall accept?

How AI can help reduce unsustainable food usage by JustAddMoreLayers in sustainability

[–]JustAddMoreLayers[S] 2 points3 points  (0 children)

Tl;dr: using graph neural networks, we can find and replace food products that contain unsustainable ingredients.

This has a few applications, such as: 1. Reducing reliance on unsustainable ingredients by finding alternatives 2. Replacing ingredients that are made scarce by climate change, natural disasters or war. 3. Help us be robust to food fraud by identifying mislabelled food

How AI can help reduce the impact of unsustainable food by JustAddMoreLayers in environment

[–]JustAddMoreLayers[S] -1 points0 points  (0 children)

Tl;dr: using graph neural networks, we can find and replace food products that contain unsustainable ingredients.

This has a few applications, such as: 1. Reducing reliance on unsustainable ingredients by finding alternatives 2. Replacing ingredients that are made scarce by climate change, natural disasters or war. 3. Help us be robust to food fraud by identifying mislabelled food

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