Artificial Intelligence – Telegram
Artificial Intelligence
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Artificial Intelligence

admin - @haarrp

@itchannels_telegram - 🔥 best it channels

@ai_machinelearning_big_data - Machine learning channel

@pythonl - Our Python channel

@pythonlbooks- python книги📚

@datascienceiot - ml 📚

РКН: clck.ru/3FmwZw
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SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-training

🖥 Github: github.com/gengdavid/slca

📕 Paper: https://arxiv.org/abs/2408.08295v1

🚀 Dataset: https://paperswithcode.com/dataset/imagenet-r

@ArtificialIntelligencedl
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Forwarded from Machinelearning
⚡️Awesome FLUX Resources: Все ресурсы по Flux в одном месте.

Экосистема Fluх развивается очень быстро, каждый день появляются новые способы, решения, возможности и инструменты для работы с моделями Fluх онлайн и оффлайн.

Теперь у сообщества FLUX появился обновляемый и упорядоченный Awesome FLUX!


https://awesomeflux.com/


🖥Github [ Stars: 16 | Issues: 0 | Forks: 1]


@ai_machinelearning_big_data

#AI #FLUX #ML #Awesome
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Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks

🖥 Github: https://github.com/tencent-ailab/hokoff

📕 Paper: https://arxiv.org/abs/2408.10556v1

🚀 Dataset: https://paperswithcode.com/dataset/d4rl

@ArtificialIntelligencedl
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SAM & SAM 2 for Medical Image Segmentation.

🖥 Github: https://github.com/yichizhang98/sam4mis

📕 Paper: https://arxiv.org/abs/2408.12889v1

@ArtificialIntelligencedl
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All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation (NeurIPS 2023)

🖥 Github: https://github.com/LiyaoTang/ERDA

📕 Paper: https://arxiv.org/abs/2408.16520v1

🚀 Dataset: https://paperswithcode.com/dataset/cityscapes

@ArtificialIntelligencedl
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⚡️ ART - Actually Robust Training

pip install art-training

🖥 Github: https://github.com/sebchw/actually-robust-training

📕 Paper: https://arxiv.org/abs/2408.16285v1

@ArtificialIntelligencedl
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aTENNuate: is a network that can be configured for real-time speech enhancement on raw audio waveforms.

🖥 Github: https://github.com/Brainchip-Inc/aTENNuate

📕 Paper: https://arxiv.org/abs/2409.03377v1

🚀 Dataset: https://paperswithcode.com/dataset/deep-noise-suppression-2020

@ArtificialIntelligencedl
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⚡️Легкий способ получать свежие обновления и следить за трендами в разработке на вашем языке. Находите свой стек и подписывайтесь:

МАШИННОЕ ОБУЧЕНИЕ: t.me/ai_machinelearning_big_data
C++ t.me/cpluspluc
Python: t.me/pro_python_code
Хакинг: t.me/linuxkalii
Devops: t.me/devOPSitsec
АНАЛИЗ Данных: t.me/data_analysis_ml
Javanoscript: t.me/javanoscriptv
C#: t.me/csharp_ci
Java: t.me/javatg
Базы данных: t.me/sqlhub
Linux: t.me/linuxacademiya
Python собеседования: t.me/python_job_interview
Мобильная разработка: t.me/mobdevelop
Docker: t.me/DevopsDocker
Golang: t.me/golang_interview
React: t.me/react_tg
Rust: t.me/rust_code
PHP: t.me/phpshka
Android: t.me/android_its
Frontend: t.me/front
Big Data: t.me/bigdatai
Собеседования МЛ: t.me/machinelearning_interview
МАТЕМАТИКА: t.me/data_math
Kubernets: t.me/kubernetc


💼 Папка с вакансиями: t.me/addlist/_zyy_jQ_QUsyM2Vi
Папка Go разработчика: t.me/addlist/MUtJEeJSxeY2YTFi
Папка Python разработчика: t.me/addlist/eEPya-HF6mkxMGIy
Папка ML: https://news.1rj.ru/str/addlist/2Ls-snqEeytkMDgy
Папка FRONTEND: https://news.1rj.ru/str/addlist/mzMMG3RPZhY2M2Iy

😆ИТ-Мемы: t.me/memes_prog
🇬🇧Английский: t.me/english_forprogrammers
🧠ИИ: t.me/vistehno

🎓954ГБ ОПЕНСОРС КУРСОВ: @courses
📕Ит-книги бесплатно: https://news.1rj.ru/str/addlist/BkskQciUW_FhNjEy
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LLaMA-Omni: Seamless Speech Interaction with Large Language Models

🖥 Github: https://github.com/ictnlp/llama-omni

📕 Paper: https://arxiv.org/abs/2409.06666

🤗 HF: https://huggingface.co/ICTNLP/Llama-3.1-8B-Omni

@ArtificialIntelligencedl
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🚀 FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity Refiner (NeurIPS 2024)

git clone https://github.com/shiml20/FlowTurbo.git
cd FlowTurbo


🖥 Github: https://github.com/shiml20/flowturbo

📕 Paper: https://arxiv.org/abs/2409.18128v1

🤗 Dataset: https://paperswithcode.com/dataset/imagenet

@ArtificialIntelligencedl
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How to Train Long-Context Language Models (Effectively)

🖥 Github: https://github.com/hijkzzz/pymarl2

📕 Paper: https://arxiv.org/abs/2410.02511v1

🤗 Dataset: https://paperswithcode.com/dataset/smac

@ArtificialIntelligencedl
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