EIGNN: Efficient Infinite-Depth Graph Neural Networks

Github: https://github.com/liu-jc/eignn

Paper: https://arxiv.org/abs/2202.10720v1

Dataset: https://paperswithcode.com/dataset/ppi

@ArtificialIntelligencedl

Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation

Github: https://github.com/tzer-anonbot/tzer

Docs: https://tzer.readthedocs.io/en/latest/markdown/artifact.html

Paper: https://arxiv.org/abs/2202.09947v1


@ai_machinelearning_big_data

Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks

Github: https://github.com/networkslab/baggraph

Paper: https://arxiv.org/abs/2202.11132v1


@ai_machinelearning_big_data

✅ As-ViT: Auto-scaling Vision Transformers without Training

Github: https://github.com/vita-group/asvit

Paper: https://arxiv.org/abs/2202.11921v1

Dataset: https://paperswithcode.com/dataset/imagenet

@ArtificialIntelligencedl

DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association

Github: https://github.com/wangxiyang2022/DeepFusionMOT

Paper: https://arxiv.org/abs/2202.12100v1

Dataset: https://paperswithcode.com/dataset/kitti

@ArtificialIntelligencedl

Overcoming catastrophic forgetting in neural networks

Github: https://github.com/ContinualAI/avalanche

Paper: https://arxiv.org/abs/1612.00796v2

Dataset: https://paperswithcode.com/dataset/asc-til-19-tasks

@ArtificialIntelligencedl

The Neural Testbed

Github: https://github.com/deepmind/neural_testbed

Paper: https://arxiv.org/abs/2202.13509v1

@ArtificialIntelligencedl

FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours

Github: https://github.com/hpcaitech/fastfold

Paper: https://arxiv.org/abs/2203.00854v1

@ArtificialIntelligencedl

💡 KG Inductive Link Prediction Challenge (ILPC) 2022

Github: https://github.com/pykeen/ilpc2022

Paper: https://arxiv.org/abs/2203.01520v1

@ArtificialIntelligencedl

Hello everyone. My name is Andrew and for several years I've been working on to make the learning path for ML
easier.

I wrote a manual on machine learning that
everyone understands - Machine Learning Simplified Book. The main purpose of my book is to build an intuitive
understanding of how algorithms work through basic examples. In order to understand the presented material,
it is enough to know basic mathematics and linear algebra.

After reading this book, you will know the basics of supervised learning, understand complex mathematical models, understand the entire pipeline of a typical ML project, and also be able to share your knowledge with colleagues from related industries and with technical
professionals.

And for those who find the theoretical part not enough - I supplemented the book with a repository on GitHub,
which has Python implementation of every method and algorithm that I describe in each chapter.

You can read the book absolutely free at the link below:
-> https://themlsbook.com

I’ve also started my Instagram page - feel free to subscribe! it’s mostly in Russian but I’ll be posting in English too.
-> https://instagram.com/5x12

KG Inductive Link Prediction Challenge (ILPC) 2022

Github: https://github.com/pykeen/ilpc2022

Paper: https://github.com/pykeen/ilpc2022

@ArtificialIntelligencedl

Как выявлять аномалии в разных распределениях с помощью машинного обучения? 🧐

10 марта в 20:00 (мск) пройдет открытый вебинар «Anomaly Detection». Его проведет Артем Васильев, ведущий инженер разработки. С экспертом мы обсудим такие вопросы, как постановка задачи, нахождение аномалий в разных распределениях, SVD-feature extraction, Autoencoder, PaDiM.

🔥 Продолжить получать новые знания вы можете на онлайн-курсе «Компьютерное зрение» для специалистов в сфере Machine Learning, которые хотят специализироваться на компьютерном зрении или систематизировать свои знания.

Чтобы участвовать, зарегистрируйтесь 👉 https://otus.pw/pd5h/

Official PyTorch implementation for Graph Matching based GNN Pre-Training.

Github: https://github.com/rucaibox/gmpt

Paper: https://arxiv.org/abs/2203.01597v1

@ArtificialIntelligencedl

Uncertainty Estimation for Heatmap-based Landmark Localization

Github: https://github.com/pykale/pykale

Documentatuin: https://github.com/pykale/pykale

Paper: https://arxiv.org/abs/2203.02351v1

Dataset: https://paperswithcode.com/dataset/kitti

@ArtificialIntelligencedl

🧊 DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

Github: https://github.com/IDEACVR/DINO

Paper: https://arxiv.org/abs/2203.03605

Dataset: https://paperswithcode.com/dataset/coco

@ArtificialIntelligencedl

New Insights on Reducing Abrupt Representation Change in Online Continual Learning

Github: https://github.com/pclucas14/aml

Paper: https://arxiv.org/abs/2203.03798v1

@ArtificialIntelligencedl

Restoring and attributing ancient texts using deep neural networks

Github: https://github.com/deepmind/ithaca

Paper: https://www.nature.com/articles/s41586-022-04448-z

Colab: https://colab.research.google.com/github/deepmind/ithaca/blob/master/colabs/ithaca_inference.ipynb

@ArtificialIntelligencedl

On Embeddings for Numerical Features in Tabular Deep Learning

Github: https://github.com/yura52/rtdl

Documentatuin: https://yura52.github.io/rtdl

Paper: https://arxiv.org/abs/2203.05556v1


@ArtificialIntelligencedl

Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement

Github: https://github.com/xuxw98/backtoreality

Paper: https://arxiv.org/abs/2203.05238v1

Dataset: https://paperswithcode.com/dataset/modelnet

@ArtificialIntelligencedl

Conditional Prompt Learning for Vision-Language Models

Github: https://github.com/kaiyangzhou/coop

Paper: https://arxiv.org/abs/2203.05557v1

Dataset: https://paperswithcode.com/dataset/imagenet

@ArtificialIntelligencedl

Enhancing Adversarial Training with Second-Order Statistics of Weights

Github: https://github.com/alexkael/s2o

Paper: https://arxiv.org/abs/2203.06020v1

Dataset: https://paperswithcode.com/dataset/cifar-10

@ArtificialIntelligencedl