Submission Guidelines for Fully Connected
A brief explainer on how to get your work featured on Fully Connected
Created on July 8|Last edited on July 8
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What is Fully Connected?
Fully Connected is Weights & Biases blog. We publish pieces from internal authors, community members, repo maintainers, researchers, paper reproducers, and a whole lot more. We believe the best content comes from diverse perspectives and we’re always looking for new authors with a keen interest in sharing their research, experiments, and thoughts with the world.
What kind of pieces do you publish?
We don’t publish thought leadership pieces or posts without much technical heft. We’re more interested in tutorials about common techniques on popular frameworks, experiments on real or toy data, model reproductions and explainers, tips on setting up model pipelines, etc. We want practical, actionable pieces that our readers will learn something from.
What other guidelines do you have?
We like to see Weights & Biases visualizations, plots, tables, and charts in our posts. You’ll submit via our Report functionality (more on that in a second) so these are all easily embeddable. We don’t want marketing posts talking about our tool—that’s not our thing. But we are interested in seeing how you use our tool and showcasing that to like-minded practitioners. We’re not shy about including math or code either.
Will you edit my work?
We have an editorial staff that works to make your pieces as good as possible as well as a staff of machine learning practitioners to check your code and W&B implementation. Edits will be made on our end and, if there are substantive changes, we reach out to you for clarification. Grammar, tone, form, and typos will be taken care of on our end.
What does a good piece look like?
Firstly, all submissions must come as Weights & Biases reports. You can learn more about those here.
A strong submission has:
- A simple introduction that spells out what you’re writing about. Think of this as your central thesis that you’ll prove throughout the piece.
- Details about the data you’re using, the model(s) you’re training, the techniques you’re employing, etc.
- A lesson or viewpoint that the reader can take away when they’ve finished.
- Some personality too–remember, this isn’t an academic paper! Emojis, relevant gifs, fun GAN implementations, even a few light-hearted jokes are encouraged.
- Above all else: your piece should strive to educate and entertain.
- A simple conclusion that summarizes your thesis
Can I see some examples?
Of course! Here are a handful of our favorite submissions from the past year or so:
MLP-Mixer: An all-MLP Architecture for Vision
In the past few months there have been various papers proposing MLP based architectures without Attention or Convolutions. This report analyses the paper 'MLP-Mixer: An all-MLP Architecture for Vision' by Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer and others.
Solving Wordle with Reinforcement Learning
In this article, we explore how to use Deep Reinforcement Learning (RL) to teach a bot to play Wordle, the word-guessing game now owned by The New York Times.
New Techniques for Generating Images With Text
In this article, we'll look at Image Generation with CLOOB Conditioned Latent Denoising Diffusion Generative Adversarial Networks (GANs), or CCLDDG, for short.
Ok, I’m ready to submit. What next?
For your first piece, we ask that you email editor@wandb.com with a link to your post. We will contact you with additional steps or questions that help clarify your submission.
Thank you!
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