🚀 Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism

Gold-YOLO, which boosts the multi-scale feature fusion capabilities and achieves an ideal balance between latency and accuracy across all model scales.


🖥 Github: https://github.com/huawei-noah/Efficient-Computing/tree/master/Detection/Gold-YOLO

📕 Paper: https://arxiv.org/abs/2309.11331v2

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

@Machine_learn

InstructionERC

🖥 Github: https://github.com/LIN-SHANG/InstructERC

📕 Paper: https://arxiv.org/pdf/2309.11911v1.pdf

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

@Machine_learn

This channels is for Programmers, Coders, Software Engineers.

0- Python
1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning
8- programming Languages

✅ Data Science Channels:
https://t.me/addlist/8_rRW2scgfRhOTc0

✅ Main Channel:
https://t.me/DataScienceM

🗣 Leveraging In-the-Wild Data for Effective Self-Supervised Pretraining in Speaker Recognition


pip3 install wespeakerruntime

🖥 Github: https://github.com/wenet-e2e/wespeaker

📕 Paper: https://arxiv.org/abs/2309.11730v1

⏩ Demo: https://huggingface.co/spaces/wenet/wespeaker_demo

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

@Machine_learn

🎓 BayesDLL: Bayesian Deep Learning Library

New Bayesian neural network library for PyTorch for large-scale deep network


🖥 Github: https://github.com/samsunglabs/bayesdll

📕 Paper: https://arxiv.org/abs/2309.12928v1

⭐️ Dataset: https://paperswithcode.com/dataset/oxford-102-flower

@Machine_learn

Book: ARTIFICIAL INTELLIGENCE (SUBJECT CODE 417) CLASS – 3
Authors: Orange Education Pvt Ltd
ISBN: Null
year: 2023
pages: 619
Tags:#AI
@Machine_learn

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

@Machine_learn

➕ 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

@Machine_learn

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

@Machine_learn

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

@Machine_learn

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

@Machine_learn

Atom2D

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

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

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

@Machine_learn

☑️ 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

@Machine_learn

🤖 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

@Machine_learn

✅️ 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

@Machine_learn

💻 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

@Machine_learn

G4SATBench

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

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

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

@Machine_learn