LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

🖥 Github: https://github.com/dvlab-research/longlora

📕 Paper: https://arxiv.org/pdf/2309.12307v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/pg-19

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➕ fastMONAI: A low-code deep learning library for medical image analysis

Simplifying deep learning for medical imaging.


git clone https://github.com/MMIV-ML/fastMONAI

🖥 Github: https://github.com/MMIV-ML/fastMONAI

Project: https://fastmonai.no

📕 Paper: https://www.sciencedirect.com/science/article/pii/S2665963823001203

🖥 Colab: https://colab.research.google.com/github/MMIV-ML/fastMONAI/blob/master/nbs/10a_tutorial_classification.ipynb

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Book: Quantum Mechanics and
Bayesian Machines
Authors: George Chapline
Lawrence Livermore National Laboratory, USA
ISBN: Null
year: 2023
pages: 194
Tags:#QM #BM
@Machine_learn

Privacy-preserving in-context learning with differentially private few-shot generation

🖥 Github: https://github.com/microsoft/dp-few-shot-generation

📕 Paper: https://arxiv.org/pdf/2309.11765v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/ag-news

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Book: Developing Apps with GPT-4 and
ChatGPT
Authors: Build Intelligent Chatbots, Content Generators, and More
ISBN: 978-1-098-15248-2
year: 2023
pages: 117
Tags:#GPT
@Machine_learn

✏️ Deep Geometrized Cartoon Line Inbetweening

Method can effectively capture the sparsity and unique structure of line drawings while preserving the details during inbetweening.

🖥 Github: https://github.com/lisiyao21/animeinbet

☑️ Demo: https://youtu.be/iUF-LsqFKpI?si=9FViAZUyFdSfZzS5

📕 Paper: https://arxiv.org/pdf/2309.16643v1.pdf

⭐️ Dataset: https://drive.google.com/file/d/1SNRGajIECxNwRp6ZJ0IlY7AEl2mRm2DR/view?usp=sharing

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Book: Generative Deep Learning
Teaching Machines to Paint, Write, Compose, and Play
Authors: David Foster
ISBN: 978-1-098-13418-1
year: 2023
pages: 456
Tags:#GAN
@Machine_learn

Class Incremental Learning via Likelihood Ratio Based Task Prediction

🖥 Github: https://github.com/linhaowei1/tplr

📕 Paper: https://arxiv.org/pdf/2309.15048v1.pdf

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

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Atom2D

🖥 Github: https://github.com/vincentx15/atom2d

📕 Paper: https://arxiv.org/pdf/2309.16519v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/atom3d

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☑️ Efficient Streaming Language Models with Attention Sinks

StreamingLLM, an efficient framework that enables LLMs trained with a finite length attention window to generalize to infinite sequence length without any fine-tuning.

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

📕 Paper: http://arxiv.org/abs/2309.17453

⭐️ Dataset: https://paperswithcode.com/dataset/pg-19

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🤖 GenSim: Generating Robotic Simulation Tasks via Large Language Models


🖥 Github: https://github.com/liruiw/gensim

✔️ Project: https://liruiw.github.io/gensim

📕 Paper: https://arxiv.org/abs/2310.01361v1

✅ Dataset: https://huggingface.co/datasets/Gen-Sim/Gen-Sim

⭐️ Demos: https://huggingface.co/spaces/Gen-Sim/Gen-Sim

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✅️ T3Bench: Benchmarking Current Progress in Text-to-3D Generation



🖥 Github: https://github.com/THU-LYJ-Lab/T3Bench

📕 Paper: https://arxiv.org/abs/2310.02977v1

⭐️ Dataset: https://paperswithcode.com/dataset/nerf

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💻 Graph Structure Learning Benchmark (GSLB)


pip install GSLB

🖥 Github: https://github.com/gsl-benchmark/gslb

📕 Paper: https://arxiv.org/abs/2310.05163v1

⭐️ Paper collection: https://github.com/GSL-Benchmark/Awesome-Graph-Structure-Learning

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G4SATBench

🖥 Github: https://github.com/zhaoyu-li/g4satbench

📕 Paper: https://arxiv.org/pdf/2309.16941v1.pdf

🔥 Tasks: https://paperswithcode.com/task/benchmarking

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Book: ArtificialIntelligence Programming
withPython F R O MZ E R OT OH E R O
Authors: Perry Xiao
ISBN: 978-1-119-82094-9 (ebk)
year: 2022
pages: 716
Tags:#AI #DL
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Code for MIMO Activation Function

🖥 Github: https://github.com/ljy9912/mimo_nn

📕 Paper: https://arxiv.org/pdf/2309.17194v1.pdf

🔥 Datasets: https://paperswithcode.com/dataset/cifar-10

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🌽 Harnessing Administrative Data Inventories to Create a Reliable Transnational Reference Database for Crop Type Monitoring


🖥 Github: https://github.com/maja601/eurocrops

📕 Paper: https://arxiv.org/pdf/2310.06393v1.pdf

⭐️ Dataset: https://syncandshare.lrz.de/getlink/fiAD95cTrXbnKMrdZYrFFcN8/

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✅ Mini-DALLE3: Interactive Text to Image by Prompting Large Language Models


🖥 Github: https://github.com/Zeqiang-Lai/Mini-DALLE3

📕 Paper: https://arxiv.org/abs/2310.07653v1

⭐️ Dataset: https://paperswithcode.com/dataset/mmlu

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AG3D: Learning to Generate 3D Avatars from 2D Image Collections (ICCV 2023)



🖥 Github: https://github.com/zj-dong/AG3D

📕 Paper: https://arxiv.org/abs/2305.02312

🚀Video: https://youtu.be/niP1YhJXEBE

⭐️ Project: https://zj-dong.github.io/AG3D/

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Book: Python for Scientists
Third Edition
Authors: JOHN M. STEWART
ISBN: 978-1-119-82094-9 (ebk)
year: 2023
pages: 301
Tags:#Python
@Machine_learn