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Tanishq Mathew Abraham Profile picture
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Aug 26 • 8 tweets • 4 min read
Research in AI is surprisingly more accessible to people with different backgrounds compared to other fields.

Anyone (w/ relevant experience) can contribute to impactful research.

Here are 5 research orgs you can join to contribute to real, open research in deep learning ↓
1. #EleutherAI

EleutherAI may be the most famous AI open research collective. Lots of great work has been released by EleutherAI, such as the Pile dataset, GPT-J, GPT-NeoX-20B, and VQGAN-CLIP.

Link → discord.gg/zBGx3azzUn
2. @LAION

This server focuses on developing new & replicating existing multimodal models and creating datasets to support these efforts. They have released the LAION-400M and LAION-5B datasets and trained their own CLIP models

Link → discord.com/invite/eq3cAMZ…
3. @openbioml

This server is focused on BioML research projects and just recently launched, and projects are just getting started, which means it's the perfect time to get involved!

Link → discord.gg/GgDBFP8ZEt
4. @CarperAI

This is a new research organization focused on preference learning and contrastive learning, with various applications from story generation to code generation and even architecture design generation.

Link → discord.com/invite/KgfkCVY…
5. @ml_collective

This is a great community known for its DLCT weekly talk series along with lots of events that are great for sharing research ideas & collaboration.

Members have published in top-tier conferences like NeurIPS, CVPR, etc.

Link → discord.gg/U2MzJYGS9P
If you like this thread, please like and retweet! 🙏

Hopefully you get the opportunity to participate in open AI research!

Follow me to stay tuned for some of the research I am working on through these research collectives 🙂 → @iScienceLuvr
A correction, the correct handle is actually @laion_ai...
It's at times like these I wish Twitter had an edit button 😅

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More from @iScienceLuvr

Tanishq Mathew Abraham Profile picture
Jun 29
Applying deep learning to pathology is quite challenging due to the sheer size of the slide images (gigapixels!).

A common approach is to divide images into smaller patches, for which deep learning features can be extracted & aggregated to provide a slide-level diagnosis (1/9)
Unfortunately, dividing into small patches limits the context to cellular features, missing out on the various levels of relevant features, like larger-scale tissue organization. (2/9)
Additionally, it is difficult to improve long-range dependencies with Transformers due to the high number of patches which makes attention calculation computationally difficult. (3/9)
Read 9 tweets
Tanishq Mathew Abraham Profile picture
Jun 13
You may have seen surreal and absurd AI-generated images like these ones...

These are all generated with an AI tool known as DALL·E mini

Let's talk about the history of #dallemini, and also *how* it works! ↓↓↓🧵 Image
First, let's clarify the different AI tools which many get confused about:

- DALL·E was an @OpenAI-developed AI project from Jan 2021

- DALL·E mini is a community-created project inspired by DALL·E
- DALL·E 2 is another @OpenAI-developed tool released in April (2/16)
@OpenAI DALL·E mini was actually originally developed about a year ago, back in July 2021.

During a programming competition organized by @huggingface (an AI company), @borisdayma & some community folks (including myself!) developed a neural network inspired by DALL·E & studied it (3/16) ImageImage
Read 16 tweets
Tanishq Mathew Abraham Profile picture
May 31
Have you seen #dalle2 and #Imagen and wondered how it works?

Both models utilize diffusion models, a new class of generative models that have overtaken GANs in terms of visual quality.

Here are 10 resources to help you learn about diffusion models ⬇ ⬇ ⬇
1. "What are Diffusion Models?" by @ari_seff
Link →

This 3blue1brown-esque YouTube video is a great introduction to diffusion models!
2. "Introduction to Diffusion Models for Machine Learning" by @r_o_connor
Link → assemblyai.com/blog/diffusion…

This article provides a great deep-dive of the theoretical foundations for Diffusion Models.
Read 13 tweets
Tanishq Mathew Abraham Profile picture
Apr 27
Awesome and surprising things you can do with Jupyter Notebooks ⬇
1. Write a full-fledged Python library!

You can write all of your code, documentation, & tests with Jupyter Notebooks & nbdev.fast.ai, all while maintaining best software practices and implementing CI/CD!

fastai deep learning library is entirely written in notebooks!
2. Create a blog!

Platforms like fastpages.fast.ai easily allow you to create blog posts from your Jupyter Notebooks, with the code cells and outputs in your post, and can even be made interactive.

I have my own such blog at tmabraham.github.io/blog
Read 8 tweets
Tanishq Mathew Abraham Profile picture
Mar 28
Yet another state-of-the-art method for text-to-image generation, this time from researchers at @MetaAI!

Link: arxiv.org/abs/2203.13131

How does it work? A short thread on this paper ⬇
The model is based on an autoregressive transformer (like DALL·E) combined with a VQGAN but utilizes several key tricks to improve the quality and also controllability of the generations. 2/10
One trick is the use of a segmentation map (referred to as a scene) and a VQGAN for the scene.

As you can see here, this provides more controllability to the generation process. 3/10
Read 10 tweets
Tanishq Mathew Abraham Profile picture
Mar 25
Given that this @kaggle ML competition recently started, I thought it would be a good opportunity to share my approach to Kaggle competing

A quick thread (1/7) 👇
The first step is to explore the data, also known as EDA. Getting a feel for the data is important to be able to derive important insights that can help you. 👨‍💻

Here is my EDA notebook for this competition:
kaggle.com/code/tanlikesm…

(2/7)
After exploring the data, the next step is to make a baseline solution. In this case, I had put together a quick pretrained baseline based on @Nils_Reimers's SentenceTransformers:

kaggle.com/code/tanlikesm…

(3/7)
Read 7 tweets

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