Hello everyone!

I work with spreadsheets a lot, doing tasks manually that are just a bit too complex for rules, but I believe they certainly fall into what ML can handle. In a nutshell, I spend 2+ hours a day going through company names, removing legal terms like “LLC” or “Limited”, and humanizing them.

For instance, I have a spreadsheet with company names and emails.

These would become (currently by hand):

What we’re doing here is:

1. Shorting names to their essence
2. Removing legal terms and words
3. Looking at domain names (in email addresses) as a clue for the “most human name”

Now, I very well believe this is something Google Cloud has capabilities for. Given the lack of programming involved with Google Cloud ML (and its potential integration with Google Sheets), I’d imagine it’s the best vehicle for this tool.

Some questions before I embark upon this journey:

1. Would your recommend I use Google Cloud ML or another tool?
2. How much data would you imagine would be necessary to train this tool? (uncleaned spreadsheets and cleaned spreadsheets)
3. Am I critically misunderstanding something here? This is pretty much my first time practically applying ML.

Thank you very much for all your help!

Hi,

I wonder why we are still trying to mimic photorealistic world by counting every reflection, polygon, tracing every ray and so on. Shouldn’t it be done in such manner that AI is just doing the job basing on photos and low polygon input like here https://assetstore.unity.com/packages/3d/characters/animals/poly-art-forest-set-128568 Also all other games like Zelda BOTW, Team Fortress 2 or even Fortnite could be easily turned by AI into photorealistic env. Shouldn’t we start thinking about doing AI accelerators (like first 3dfx cards) for enriching low polygonic world’s generated easily by most commodity hardware? I guess even ray tracing could be made by ML. I believed that future belongs to generating world by AI not by tricky mathematic graphics algorithms. Especially that in future it is easier to go from such trained networks into environment where instead of heaving an output on display, the output would be “drawn” directly in human brain through neural-connectivity. Also AI is able to properly handle cases where object is moving fast or turning around.

Cheers, Alexa

I’m writing my master’s thesis on image generation and was wondering: which other methods were/are used to synthesize images, aside from GANs? Any time I look up “image synthesis” I only find GAN tutorials, and excluding GANs seems to bring up photon scanning.

I thought this project from FB Research is a really cool work:

Abstract

In order to provide an immersive visual experience, modern displays require head mounting, high image resolution, low latency, as well as high refresh rate. This poses a challenging computational problem. On the other hand, the human visual system can consume only a tiny fraction of this video stream due to the drastic acuity loss in the peripheral vision. Foveated rendering and compression can save computations by reducing the image quality in the peripheral vision. However, this can cause noticeable artifacts in the periphery, or, if done conservatively, would provide only modest savings. In this work, we explore a novel foveated reconstruction method that employs the recent advances in generative adversarial neural networks. We reconstruct a plausible peripheral video from a small fraction of pixels provided every frame. The reconstruction is done by finding the closest matching video to this sparse input stream of pixels on the learned manifold of natural videos. Our method is more efficient than the state-of-the-art foveated rendering, while providing the visual experience with no noticeable quality degradation. We conducted a user study to validate our reconstruction method and compare it against existing foveated rendering and video compression techniques. Our method is fast enough to drive gaze-contingent head-mounted displays in real time on modern hardware. We plan to publish the trained network to establish a new quality bar for foveated rendering and compression as well as encourage follow-up research.

Project page / paper / video / code: https://research.fb.com/publications/deepfovea-neural-reconstruction-for-foveated-rendering-and-video-compression-using-learned-statistics-of-natural-videos/

Are there papers that do so? ImageNet images have various resolutions. Since we already have adaptive pooling techniques such as global average pooling, attention-based pooling, etc. that support variable resolution, should it be possible?

Random fixed size crop and resize seems hacky.

Hi Everyone!

New to reddit, but I’d like to share a project I’ve been working on called NBoost. It’s essentially a proxy for search APIs (e.g. Elasticsearch) that reranks search results using finetuned models (e.g. BERT).

Check out our medium article or github to learn more!

It’s main features include: – Super easy to set up (you can just pip install nboost) – Easy, non-invasive integration with Elasticsearch and potentially other search APIs. – Finetuned models are plugins (you can swap them in and out). – Fast and scaleable (written at the lowest level possible)

This is the paper: Information-theoretical label embeddings for large-scale image classification

https://arxiv.org/abs/1607.05691

I often read from ML researchers, but more from computational cognitive scientists, that humans are able to generalize patterns from only a few data points or use “rich, informative priors” even as children, and how that is very important for us as cognitive beings that sets us apart from the current neural network approaches to RL used today.

I’m also not entirely convinced that the current neural net paradigm with the McCulloch–Pitts-esque neurons is ever going to become sample efficient enough for real-world reinforcement learning tasks. It seems like despite our best efforts to increase sample efficiency in NN techniques, the most impressive results still use hundreds of thousands or more simulations/data points that could be infeasible to implement for any sufficiently complex real-world environments.

That being said, what approaches are you most excited for in reducing sample efficiency in reinforcement learning or in neural network techniques in general?