Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models

🖥 Github: https://github.com/daochenzha/neuroshard

⏩ Paper: https://arxiv.org/pdf/2305.01868v1.pdf

https://t.iss.one/DataScienceT

Multimodal Data Augmentation for Image Captioning using Diffusion Models

🖥 Github: https://github.com/xiaochr/multimodal-augmentation-image-captioning

⏩ Paper: https://arxiv.org/pdf/2305.01855v1.pdf

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

https://t.iss.one/DataScienceT

⭐️ Towards Building the Federated GPT: Federated Instruction Tuning

Shepherd: A lightweight, foundational framework enabling federated instruction tuning for large language models

🖥 Github: https://github.com/jayzhang42/federatedgpt-shepherd

⏩ Paper: https://arxiv.org/pdf/2305.05644.pdf

📌 Data Preparation: https://github.com/jayzhang42/federatedgpt-shepherd#Data_Preparation

https://t.iss.one/DataScienceT

ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding

You can easily plug in any 3D backbone models and pre-train it using our framework to get a jump-start for various downstreaming tasks!

🖥 Github: https://github.com/salesforce/ulip

⏩ Paper: https://arxiv.org/abs/2305.08275v1

📌 Dataset: https://paperswithcode.com/dataset/objaverse

https://t.iss.one/DataScienceT

FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention

FastComposer uses subject embeddings extracted by an image encoder to augment the generic text conditioning in diffusion models, enabling personalized image generation based on subject images and textual instructions with only forward passes.

🖥 Github: https://github.com/mit-han-lab/fastcomposer

⏩ Paper: https://arxiv.org/abs/2305.10431v1

📌 Dataset: https://paperswithcode.com/dataset/ffhq

⭐️ Project: https://fastcomposer.mit.edu/

https://t.iss.one/DataScienceT

Hybrid and Collaborative Passage Reranking

🖥 Github: https://github.com/zmzhang2000/hybrank

⏩ Paper: https://arxiv.org/pdf/2305.09313v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/natural-questions

https://t.iss.one/DataScienceT

FunASR: A Fundamental End-to-End Speech Recognition Toolkit

FunASR, an open-source speech recognition toolkit designed to bridge the gap between academic research and industrial applications

🖥 Github: https://github.com/alibaba-damo-academy/FunASR

⭐️ Docs: https://alibaba-damo-academy.github.io/FunASR/en/index.html

⏩ Paper: https://arxiv.org/abs/2305.11013v1

📌 Dataset: https://paperswithcode.com/dataset/wenetspeech

https://t.iss.one/DataScienceT

Segment Any Anomaly without Training via Hybrid Prompt Regularization

This project addresses zero-shot anomaly detection by combining SAM and Grouding DINO.

🖥 Github: https://github.com/caoyunkang/segment-any-anomaly

🖥 Colab: https://colab.research.google.com/drive/1Rwio_KfziuLp79Qh_ugum64Hjnq4ZwsE?usp=sharing

⏩ Paper: https://arxiv.org/abs/2305.11013v1

📌 Dataset: https://paperswithcode.com/dataset/visa

https://t.iss.one/DataScienceT

Diff-Pruning: Structural Pruning for Diffusion Models

Structural Pruning for Diffusion Models.

🖥 Github: https://github.com/vainf/diff-pruning

⏩ Paper: https://arxiv.org/abs/2305.10924v1

📌 Dataset: https://paperswithcode.com/dataset/lsun

https://t.iss.one/DataScienceT

🔥 Here's a list of 32 datasets that you can go over the weekend:
https://datasciencedojo.com/blog/datasets-data-science-skills/

✅ More reaction = more projects

@CodeProgrammer ♥️

How to Encrypt and Decrypt Image Using Python | How to Encrypt any Image File Using Python
https://morioh.com/p/978e38a1f65b?f=5c21fb01c16e2556b555ab32

✅ More reaction = more projects

@CodeProgrammer ♥️

Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold

Paper:
https://arxiv.org/abs/2305.10973

Github:
https://github.com/XingangPan/DragGAN

Project page:
https://vcai.mpi-inf.mpg.de/projects/DragGAN/

https://t.iss.one/DataScienceT

🦙 LLM-Pruner: On the Structural Pruning of Large Language Models

Compress your LLMs to any size;

🖥 Github: https://github.com/horseee/llm-pruner

⏩ Paper: https://arxiv.org/abs/2305.11627v1

📌 Dataset: https://paperswithcode.com/dataset/piqa

https://t.iss.one/DataScienceT

Mask-Free Video Instance Segmentation

MaskFreeVIS, achieving highly competitive VIS performance, while only using bounding box annotations for the object state.

🖥 Github: https://github.com/SysCV/maskfreevis

⏩ Paper: https://arxiv.org/pdf/2303.15904.pdf

📌 Project: https://www.vis.xyz/pub/maskfreevis/

https://t.iss.one/DataScienceT

QLoRA: Efficient Finetuning of Quantized LLMs

Model name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99.3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU.

🖥 Github: https://github.com/artidoro/qlora

⏩ Paper: https://arxiv.org/abs/2305.14314

⭐️ Demo: https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi

📌 Dataset: https://paperswithcode.com/dataset/ffhq

https://t.iss.one/DataScienceT

Large Language Models as Tool Makers

In this work, we take an initial step towards removing this dependency by proposing a closed-loop framework, referred to as LLMs A s Tool Makers (LATM), where LLMs create their own reusable tools for problem-solving.

🖥 Github: https://github.com/ctlllll/llm-toolmaker

⏩ Paper: https://arxiv.org/pdf/2305.17126v1.pdf

📌 Dataset: https://paperswithcode.com/dataset/big-bench

https://t.iss.one/DataScienceT