OmniDocBench is a benchmark for evaluating diverse document parsing in real-world scenarios, featuring the following characteristics
🖥 Github: https://github.com/opendatalab/OmniDocBench
📕 Paper: https://arxiv.org/abs/2412.07626
🌟 Dataset: https://huggingface.co/datasets/opendatalab/OmniDocBench
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🌟 Dataset: https://huggingface.co/datasets/opendatalab/OmniDocBench
@ArtificialIntelligencedl
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2DMatGMM: An open-source robust machine learning platform for real-time detection and classification of 2D material flakes
🖥 Github: https://github.com/jaluus/2dmatgmm
📕 Paper: https://arxiv.org/abs/2412.09333v1
🌟 Dataset: https://paperswithcode.com/task/instance-segmentation
@ArtificialIntelligencedl
🌟 Dataset: https://paperswithcode.com/task/instance-segmentation
@ArtificialIntelligencedl
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⚡️ Byte Latent Transformer: Patches Scale Better Than Tokens
Byte Latent Transformer architecture (BLTs), a new byte-level LLM architecture that for the first time, matches tokenization-based LLM performance at scale, with significant improvements in inference efficiency and robustness.
🖥 Github: https://github.com/facebookresearch/blt
📕 Paper: https://arxiv.org/abs/2412.09871v1
🌟 Dataset: https://paperswithcode.com/dataset/mmlu
@ArtificialIntelligencedl
Byte Latent Transformer architecture (BLTs), a new byte-level LLM architecture that for the first time, matches tokenization-based LLM performance at scale, with significant improvements in inference efficiency and robustness.
🌟 Dataset: https://paperswithcode.com/dataset/mmlu
@ArtificialIntelligencedl
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🀄 GuoFeng Webnovel: A Discourse-Level and Multilingual Corpus of Web Fiction
🖥 Github: https://github.com/longyuewangdcu/guofeng-webnovel
📕 Paper: https://arxiv.org/abs/2412.11732v1
🌟 Dataset: www2.statmt.org/wmt24/literary-trans
@ArtificialIntelligencedl
🌟 Dataset: www2.statmt.org/wmt24/literary-trans
@ArtificialIntelligencedl
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Large Language Models Course: Learn by Doing LLM Projects
🖥 Github: https://github.com/peremartra/Large-Language-Model-Notebooks-Course
📕 Paper: https://doi.org/10.31219/osf.io/qgxea
@ArtificialIntelligencedl
@ArtificialIntelligencedl
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Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs
🖥 Github: https://github.com/zhouyiks/CoLVA/tree/main
📕 Paper: https://arxiv.org/pdf/2501.04670v1.pdf
🌟 Dataset: https://paperswithcode.com/dataset/bdd100k
@ArtificialIntelligencedl
🌟 Dataset: https://paperswithcode.com/dataset/bdd100k
@ArtificialIntelligencedl
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Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding
🖥 Github: https://github.com/opengvlab/piip
📕 Paper: https://arxiv.org/abs/2501.07783v1
🌟 Dataset: https://paperswithcode.com/dataset/gqa
@ArtificialIntelligencedl
🌟 Dataset: https://paperswithcode.com/dataset/gqa
@ArtificialIntelligencedl
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Continual Forgetting for Pre-trained Vision Models (CVPR2024)
🖥 Github: https://github.com/bjzhb666/GS-LoRA
📕 Paper: https://arxiv.org/abs/2501.09705v1
🧠 Dataset: https://paperswithcode.com/dataset/coco
@ArtificialIntelligencedl
@ArtificialIntelligencedl
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@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: www.tgoop.com/ai_machinelearning_big_data
C++ www.tgoop.com/cpluspluc
Python: www.tgoop.com/pythonl
Linux: www.tgoop.com/linuxacademiya
Хакинг: https://www.tgoop.com/+i__6ED-eRfkwOTYy
Devops: www.tgoop.com/DevOPSitsec
Data Science: www.tgoop.com/data_analysis_ml
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Мобильная разработка: www.tgoop.com/mobdevelop
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Frontend: www.tgoop.com/front
Big Data: www.tgoop.com/bigdatai
Собеседования МЛ: www.tgoop.com/machinelearning_interview
МАТЕМАТИКА: www.tgoop.com/data_math
Kubernets: www.tgoop.com/kubernetc
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Haskell: www.tgoop.com/haskell_tg
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💼 Папка с вакансиями: www.tgoop.com/addlist/_zyy_jQ_QUsyM2Vi
Папка Go разработчика: www.tgoop.com/addlist/MUtJEeJSxeY2YTFi
Папка Python разработчика: www.tgoop.com/addlist/eEPya-HF6mkxMGIy
Папка ML: https://www.tgoop.com/addlist/2Ls-snqEeytkMDgy
Папка FRONTEND: https://www.tgoop.com/addlist/mzMMG3RPZhY2M2Iy
😆ИТ-Мемы: www.tgoop.com/memes_prog
🇬🇧Английский: www.tgoop.com/english_forprogrammers
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🎓954ГБ ОПЕНСОРС КУРСОВ: @courses
📕Ит-книги бесплатно: https://www.tgoop.com/addlist/BkskQciUW_FhNjEy
МАШИННОЕ ОБУЧЕНИЕ: www.tgoop.com/ai_machinelearning_big_data
C++ www.tgoop.com/cpluspluc
Python: www.tgoop.com/pythonl
Linux: www.tgoop.com/linuxacademiya
Хакинг: https://www.tgoop.com/+i__6ED-eRfkwOTYy
Devops: www.tgoop.com/DevOPSitsec
Data Science: www.tgoop.com/data_analysis_ml
Javascript: www.tgoop.com/javascriptv
C#: www.tgoop.com/csharp_ci
Java: www.tgoop.com/javatg
Базы данных: www.tgoop.com/sqlhub
Python собеседования: www.tgoop.com/python_job_interview
Мобильная разработка: www.tgoop.com/mobdevelop
Docker: www.tgoop.com/DevopsDocker
Golang: www.tgoop.com/Golang_google
React: www.tgoop.com/react_tg
Rust: www.tgoop.com/rust_code
ИИ: www.tgoop.com/vistehno
PHP: www.tgoop.com/phpshka
Android: www.tgoop.com/android_its
Frontend: www.tgoop.com/front
Big Data: www.tgoop.com/bigdatai
Собеседования МЛ: www.tgoop.com/machinelearning_interview
МАТЕМАТИКА: www.tgoop.com/data_math
Kubernets: www.tgoop.com/kubernetc
Разработка игр: https://www.tgoop.com/gamedev
Haskell: www.tgoop.com/haskell_tg
Физика: www.tgoop.com/fizmat
💼 Папка с вакансиями: www.tgoop.com/addlist/_zyy_jQ_QUsyM2Vi
Папка Go разработчика: www.tgoop.com/addlist/MUtJEeJSxeY2YTFi
Папка Python разработчика: www.tgoop.com/addlist/eEPya-HF6mkxMGIy
Папка ML: https://www.tgoop.com/addlist/2Ls-snqEeytkMDgy
Папка FRONTEND: https://www.tgoop.com/addlist/mzMMG3RPZhY2M2Iy
😆ИТ-Мемы: www.tgoop.com/memes_prog
🇬🇧Английский: www.tgoop.com/english_forprogrammers
🧠ИИ: www.tgoop.com/vistehno
🎓954ГБ ОПЕНСОРС КУРСОВ: @courses
📕Ит-книги бесплатно: https://www.tgoop.com/addlist/BkskQciUW_FhNjEy
WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in Post-Training
🖥 Github: https://github.com/penfever/wildchat-50m
📕 Paper: https://arxiv.org/abs/2501.18511v1
🧠 Dataset: https://huggingface.co/collections/nyu-dice-lab/wildchat-50m-679a5df2c5967db8ab341ab7
@ArtificialIntelligencedl
@ArtificialIntelligencedl
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CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning
🖥 Github: https://github.com/chumingqian/CycleGuardian
📕 Paper: https://arxiv.org/abs/2502.00734v1
🌟 Dataset: https://paperswithcode.com/dataset/icbhi-respiratory-sound-database
@ArtificialIntelligencedl
🌟 Dataset: https://paperswithcode.com/dataset/icbhi-respiratory-sound-database
@ArtificialIntelligencedl
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🌟 Dataset: https://paperswithcode.com/task/image-relighting
@ArtificialIntelligencedl
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