🎲 Anti-Exploration by Random Network Distillation, Tinkoff Research, ICML 2023

We propose a new ensemble-free offline RL algorithm called SAC-RND. We evaluate our method on the D4RL (Fu et al., 2020) benchmark, and show that SAC-RND achieves performance comparable to ensemble-based methods while outperforming ensemble-free approaches.


🖥 Github: https://github.com/tinkoff-ai/sac-rnd

🤓 Paper: https://proceedings.mlr.press/v202/nikulin23a.html

@Machine_learn

Book: Machine Learning: The Basics
Authors: Alexander Jung
ISBN: -
year: 2023
pages: 287
Tags:#ML
@Machine_learn

🚀 AgentBench: Evaluating LLMs as Agents.

AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting.


🖥 Github: https://github.com/thudm/agentbench

📕 Paper: https://arxiv.org/abs/2308.03688v1

☑️ Dataset: https://paperswithcode.com/dataset/alfworld

@Machine_learn

✅ SSLRec: A Self-Supervised Learning Library for Recommendation

SSLRec, a novel benchmark platform that provides a standardized, flexible, and comprehensive framework for evaluating various SSL-enhanced recommenders.


🖥 Github: https://github.com/hkuds/sslrec

📕 Paper: https://arxiv.org/abs/2308.05697v1

⛓ Models: https://github.com/HKUDS/SSLRec/blob/main/docs/Models.md

☑️ Datasets: https://github.com/HKUDS/SSLRec/blob/main/docs/Models.md

ai_machinelearning_big_data

LightTBNet

🖥 Github: https://github.com/dani-capellan/LightTBNet

📕 Paper: https://arxiv.org/pdf/2309.02140v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/montgomery-county-x-ray-set

@Machine_learn

LLM_Fine_Tuning_Molecular_Properties

🖥 Github: https://github.com/SylwiaNowakowska/LLM_Fine_Tuning_Molecular_Properties

📕 Paper: https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/65030b55b338ec988a780108/original/chem-ber-ta-2-fine-tuning-for-molecule-s-hiv-replication-inhibition-prediction.pdf

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

@Machine_learn

🚀 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