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⏪ Papers with Code: Year in Review
We’re ending the year by taking a look back at the top trending machine learning papers, libraries and new datasets for 2021. Read on below!
https://t.co/2tUWUgrZKp
Deep learning models for tabular data continue to improve. What are the latest methods and recent progress?
Let’s have a look ↓
Graph neural networks are rapidly advancing progress in ML for complex graph data applications.
In this week’s newsletter, we show you recent uses of GNNs ranging from protein interface prediction to collaborative filtering! Read on below…
🎉 We've just crossed 5000 Datasets! 🎉
We now index and organize more than 5000 research datasets for machine learning. A huge thanks to the research community for their ongoing contributions.
Browse the full catalogue here: https://t.co/…
Graph neural networks are driving lots of progress in machine learning by extending deep learning approaches to complex graph data and applications.
Let’s take a look at a few methods ↓
🎉 Introducing Datasets on arXiv! 🎉
The new "Code & Data" tab shows datasets used and introduced in the paper!
Read more: https://t.co/FwacSa3IBy
🎉 Introducing @paperswithdata 🎉
A curated, daily feed of newly published datasets in machine learning.
Follow to stay updated with new datasets!
🎉 Introducing...Papers with Libraries! We are now indexing ML libraries from the community. We start with the amazing TIMM by @wightmanr. Explore 330+ models, visualize architectures, compare results and hyperparameters, and so much more! …
🎉 Introducing... Datasets! We are now indexing 3000+ research datasets from machine learning. Find datasets by task and modality, compare usage over time, browse benchmarks, and much more! Explore the catalogue here:
⏪ Papers with Code: Year in Review. We’re ending the year by taking a look back at the top trending papers, libraries and benchmarks for 2020. Read on below!
https://t.co/jHoK4Cl94S
🎉 Papers with Code is expanding to more sciences! Today we launch new sites for physics, maths, CS, statistics and astronomy. You can use these sites to sync your code to show on arXiv. Explore our portal here:
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- Organic base — far more followers than accounts it follows.
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- 2.15% engagement — below the ~14.4% typical for this size.
Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.
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