Data Science | Machine Learning with Python for Researchers
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The Data Science and Python channel is for researchers and advanced programmers

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Datasets

Datasets collected for network science, deep learning and general machine learning research.

Github: https://github.com/benedekrozemberczki/datasets

Paper: https://arxiv.org/abs/2101.03091v1

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@DataScience_Books
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Multivariate Probabilistic Time Series Forecasting with Informer

Efficient transformer-based model for LSTF.

Method introduces a Probabilistic Attention mechanism to select the β€œactive” queries rather than the β€œlazy” queries and provides a sparse Transformer thus mitigating the quadratic compute and memory requirements of vanilla attention.

πŸ€—Hugging face:
https://huggingface.co/blog/informer

⏩ Paper:
https://huggingface.co/docs/transformers/main/en/model_doc/informer

⭐️ Colab:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multivariate_informer.ipynb

πŸ’¨ Dataset:
https://huggingface.co/docs/datasets/v2.7.0/en/package_reference/main_classes#datasets.Dataset.set_transform

https://t.iss.one/DataScienceT
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Linear Algebra in Python: Matrix Inverses and Least Squares

https://realpython.com/python-linear-algebra/
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Tuned Lens πŸ”Ž

Simple interface training and evaluating tuned lenses. A tuned lens allows us to peak at the iterative computations a transformer uses to compute the next token.

pip install tuned-lens

πŸ–₯ Github: https://github.com/alignmentresearch/tuned-lens

⏩ Paper: https://arxiv.org/abs/2303.08112v1

⭐️ Dataset: https://paperswithcode.com/dataset/the-pile

πŸ–₯ Colab: https://colab.research.google.com/github/AlignmentResearch/tuned-lens/blob/main/notebooks/interactive.ipynb

https://t.iss.one/DataScienceT
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OpenSeeD

A Simple Framework for Open-Vocabulary Segmentation and Detection

πŸ–₯ Github: https://github.com/idea-research/openseed

⏩ Paper: https://arxiv.org/abs/2303.08131v2

πŸ’¨ Dataset: https://paperswithcode.com/dataset/objects365

https://t.iss.one/DataScienceT
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Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank

πŸ–₯ Github: https://github.com/huang-shirui/semi-uir

⏩ Paper: https://arxiv.org/abs/2303.09101v1

πŸ’¨ Project: https://paperswithcode.com/dataset/uieb

https://t.iss.one/DataScienceT
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πŸ–₯ GigaGAN - Pytorch

Implementation of GigaGAN, new SOTA GAN out of Adobe.

https://github.com/lucidrains/gigagan-pytorch

https://t.iss.one/DataScienceT
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Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation (CVPR 2023)

Novel Diffusion Audio-Gesture Transformer is devised to better attend to the information from multiple modalities and model the long-term temporal dependency.

πŸ–₯ Github: https://github.com/advocate99/diffgesture

⏩ Paper: https://arxiv.org/abs/2303.09119v1

πŸ’¨ Dataset: https://paperswithcode.com/dataset/beat

https://t.iss.one/DataScienceT
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Deep Metric Learning for Unsupervised CD

πŸ–₯ Github: https://github.com/wgcban/metric-cd

⏩ Paper: https://arxiv.org/abs/2303.09536v1

https://t.iss.one/DataScienceT
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⚜️ ViperGPT: Visual Inference via Python Execution for Reasoning

ViperGPT, a framework that leverages code-generation models to compose vision-and-language models into subroutines to produce a result for any query.

πŸ–₯ Github: https://github.com/cvlab-columbia/viper

⏩ Paper: https://arxiv.org/pdf/2303.08128.pdf

πŸ’¨ Project: https://paperswithcode.com/dataset/beat

https://t.iss.one/DataScienceT
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πŸŽ₯ Zero-1-to-3: Zero-shot One Image to 3D Object

Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image.

πŸ–₯ Github: https://github.com/cvlab-columbia/zero123

πŸ€— Hugging face: https://huggingface.co/spaces/cvlab/zero123-live

⏩ Paper: https://arxiv.org/abs/2303.11328v1

⏩ Dataset: https://zero123.cs.columbia.edu/

πŸ’¨ Project: https://paperswithcode.com/dataset/beat

⭐️ Demo: https://huggingface.co/spaces/cvlab/zero123

https://t.iss.one/DataScienceT
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MIT Introduction to Deep Learning - 2023 Starting soon! MIT Intro to DL is one of the most concise AI courses on the web that cover basic deep learning techniques, architectures, and applications.

2023 lectures are starting in just one day, Jan 9th!

Link to register:
https://introtodeeplearning.com

MIT Introduction to Deep Learning The 2022 lectures can be found here:

https://m.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

https://t.iss.one/DataScienceT
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Train your ControlNet with diffusers 🧨

ControlNet is a neural network structure that allows fine-grained control of diffusion models by adding extra conditions.

πŸ€— Hugging face: https://huggingface.co/blog/train-your-controlnet#

πŸ–₯ Github: https://github.com/huggingface/blog/blob/main/train-your-controlnet.md

⏩ ControlNet training example: https://github.com/huggingface/diffusers/tree/main/examples/controlnet

https://t.iss.one/DataScienceT
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πŸ”₯ Fix the Noise: Disentangling Source Feature for Controllable Domain Translation

A new approach for high-quality domain translation with better controllability.

πŸ–₯ Github: https://github.com/LeeDongYeun/FixNoise

⏩ Paper: https://arxiv.org/abs/2303.11545v1

πŸ’¨ Dataset: https://paperswithcode.com/dataset/metfaces

https://t.iss.one/DataScienceT
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Text2Video-Zero

Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators

Paper: https://arxiv.org/abs/2303.13439
Video Result: video result link
Source code: https://github.com/picsart-ai-research/text2video-zero

https://t.iss.one/DataScienceT
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Conditional Image-to-Video Generation with Latent Flow Diffusion Models

New approach for cI2V using novel latent flow diffusion models (LFDM) that synthesize an optical flow sequence in the latent space based on the given condition to warp the given image.

πŸ–₯ Github: https://github.com/nihaomiao/cvpr23_lfdm

⏩ Paper: https://arxiv.org/abs/2303.13744v1

πŸ’¨ Dataset: https://drive.google.com/file/d/1dRn1wl5TUaZJiiDpIQADt1JJ0_q36MVG/view?usp=share_link

https://t.iss.one/DataScienceT
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