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TabEAE

🖥 Github: https://github.com/stardust-hyx/tabeae

⏩ Paper: https://arxiv.org/pdf/2306.00502v1.pdf

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

https://t.iss.one/DataScienceT

🔭 GRES: Generalized Referring Expression Segmentation

New benchmark (GRES), which extends the classic RES to allow expressions to refer to an arbitrary number of target objects.

🖥 Github: https://github.com/henghuiding/ReLA

⏩ Paper: https://arxiv.org/abs/2306.00968

🔎 Project: https://henghuiding.github.io/GRES/

📌 New dataset: https://github.com/henghuiding/gRefCOCO

https://t.iss.one/DataScienceT

🦍 Gorilla: Large Language Model Connected with Massive APIs

Gorilla a finetuned LLaMA-based model that surpasses the performance of GPT-4 on writing API calls.

🖥 Github: https://github.com/ShishirPatil/gorilla

📕 Paper: https://arxiv.org/abs/2305.15334

🔗 Demo: https://drive.google.com/file/d/1E0k5mG1mTiaz0kukyK1PdeohJipTFh6j/view?usp=share_link

👉 Project: https://shishirpatil.github.io/gorilla/

⭐️ Colab: https://colab.research.google.com/drive/1DEBPsccVLF_aUnmD0FwPeHFrtdC0QIUP?usp=sharing

https://t.iss.one/DataScienceT

Segment Anything 3D

SAM-3D: A toolbox transfers 2D SAM segments into 3D scene-level point clouds.

🖥 Github: https://github.com/pointcept/segmentanything3d

⏩ Paper: https://arxiv.org/abs/2306.03908v1

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

https://t.iss.one/DataScienceT

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🐼 PandaLM: ReProducible and Automated Language Model Assessment

Judge large language model, named PandaLM, which is trained to distinguish the superior model given several LLMs. PandaLM's focus extends beyond just the objective correctness of responses, which is the main focus of traditional evaluation datasets.

🖥 Github: https://github.com/weopenml/pandalm

📕 Paper: https://arxiv.org/abs/2306.05087v1

🔗 Dataset: https://github.com/tatsu-lab/stanford_alpaca#data-release

https://t.iss.one/DataScienceT

📹 Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

LLaMA is working on empowering large language models with video and audio understanding capability.

🖥 Github: https://github.com/damo-nlp-sg/video-llama

📕 Paper: https://arxiv.org/abs/2306.02858

⏩ Demo: https://huggingface.co/spaces/DAMO-NLP-SG/Video-LLaMA

📌 Model: https://modelscope.cn/studios/damo/video-llama/summary

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🏔️ Large Language Model for Geoscience

We introduce K2 (7B), an open-source language model trained by firstly further pretraining LLaMA on collected and cleaned geoscience literature, including geoscience open-access papers and Wikipedia pages, and secondly fine-tuning with knowledge-intensive instruction tuning data (GeoSignal).

git clone https://github.com/davendw49/k2.git
cd k2
conda env create -f k2.yml
conda activate k2

🖥 Github: https://github.com/davendw49/k2

⭐️ Demo: https://huggingface.co/daven3/k2_fp_delta

📕 Paper: https://arxiv.org/abs/2306.05064v1

🔗 Dataset: https://huggingface.co/datasets/daven3/geosignal

https://t.iss.one/DataScienceT

💲 FinGPT: Open-Source Financial Large Language Models

Unlike proprietary models, FinGPT takes a data-centric approach, providing researchers and practitioners with accessible and transparent resources to develop their FinLLMs.

🖥 Github: https://github.com/ai4finance-foundation/fingpt

⭐️ FinNLP: https://github.com/ai4finance-foundation/finnlp

📕 Paper: https://arxiv.org/abs/2306.06031v1

🔗 Project: https://ai4finance-foundation.github.io/FinNLP/

https://t.iss.one/DataScienceT

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GP-UNIT - Official PyTorch Implementation

🖥 Github: https://github.com/williamyang1991/gp-unit

⏩ Paper: https://arxiv.org/pdf/2306.04636v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/celeba-hq

https://t.iss.one/DataScienceT

🧔 4DHumans: Reconstructing and Tracking Humans with Transformers

Fully "transformerized" version of a network for human mesh recovery.

🖥 Github: https://github.com/shubham-goel/4D-Humans

⭐️ Colab: https://colab.research.google.com/drive/1Ex4gE5v1bPR3evfhtG7sDHxQGsWwNwby?usp=sharing

📕 Paper: https://arxiv.org/pdf/2305.20091.pdf

🔗 Project: https://shubham-goel.github.io/4dhumans/

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⚡ 21 Must-Have Cheat Sheets for Data Science Interviews: Unlocking Your Path to Success

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1. SQL Basics Cheat Sheet
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16. A Comprehensive Statistics Cheat Sheet for Data Science Interviews
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Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement
at 100k Steps-Per-Second

🖥 Github: https://github.com/facebookresearch/galactic

⏩ Paper: https://arxiv.org/pdf/2306.07552v1.pdf

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

https://t.iss.one/DataScienceT