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
@ArtificialIntelligencedl
🔥 Dataset: https://paperswithcode.com/dataset/moleculenet
@ArtificialIntelligencedl
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LLMKE
🖥 Github: https://github.com/bohuizhang/llmke
📕 Paper: https://arxiv.org/pdf/2309.08491v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/lama
@ArtificialIntelligencedl
🔥 Dataset: https://paperswithcode.com/dataset/lama
@ArtificialIntelligencedl
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Distillation-in-dg
🖥 Github: https://github.com/vorobeevich/distillation-in-dg
📕 Paper: https://arxiv.org/pdf/2309.11446v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/office-home
@ArtificialIntelligencedl
🔥 Dataset: https://paperswithcode.com/dataset/office-home
@ArtificialIntelligencedl
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InstructionERC
🖥 Github: https://github.com/LIN-SHANG/InstructERC
📕 Paper: https://arxiv.org/pdf/2309.11911v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/iemocap
@ArtificialIntelligencedl
🔥 Dataset: https://paperswithcode.com/dataset/iemocap
@ArtificialIntelligencedl
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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
@ArtificialIntelligencedl
🔥 Dataset: https://paperswithcode.com/dataset/pg-19
@ArtificialIntelligencedl
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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
@ArtificialIntelligencedl
🔥 Dataset: https://paperswithcode.com/dataset/ag-news
@ArtificialIntelligencedl
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Attack-Bard
🖥 Github: https://github.com/thu-ml/attack-bard
📕 Paper: https://arxiv.org/pdf/2309.11751v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/ffhq
@ArtificialIntelligencedl
🔥 Dataset: https://paperswithcode.com/dataset/ffhq
@ArtificialIntelligencedl
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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
@ArtificialIntelligencedl
🔥 Dataset: https://paperswithcode.com/dataset/cifar-10
@ArtificialIntelligencedl
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Augment to Interpret
🖥 Github: https://github.com/euranova/augment_to_interpret
📕 Paper: https://arxiv.org/pdf/2309.16564v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/cora
@ArtificialIntelligencedl
🔥 Dataset: https://paperswithcode.com/dataset/cora
@ArtificialIntelligencedl
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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
@ArtificialIntelligencedl
🔥 Dataset: https://paperswithcode.com/dataset/atom3d
@ArtificialIntelligencedl
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Controlling Vision-Language Models for Universal Image Restoration
🖥 Github: https://github.com/algolzw/daclip-uir
📕 Paper: https://arxiv.org/pdf/2310.01018v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/synthetic-rain-datasets
@ArtificialIntelligencedl
🔥 Dataset: https://paperswithcode.com/dataset/synthetic-rain-datasets
@ArtificialIntelligencedl
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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
@ArtificialIntelligencedl
🔥 Tasks: https://paperswithcode.com/task/benchmarking
@ArtificialIntelligencedl
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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
@ArtificialIntelligencedl
🔥 Datasets: https://paperswithcode.com/dataset/cifar-10
@ArtificialIntelligencedl
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Incremental Transfer Learning (ITL) Survey
🖥 Github: https://github.com/yixinghuang/itlsurvey
📕 Paper: https://arxiv.org/pdf/2309.17192v1.pdf
🔥 Datasets: https://paperswithcode.com/dataset/tiny-imagenet
@ArtificialIntelligencedl
🔥 Datasets: https://paperswithcode.com/dataset/tiny-imagenet
@ArtificialIntelligencedl
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[ICCV 2023] Multi-task View Synthesis with Neural Radiance Fields
🖥 Github: https://github.com/zsh2000/muvienerf
📕 Paper:https://arxiv.org/pdf/2309.17450v1.pdf
🔥 Datasets: https://paperswithcode.com/dataset/nerf
↪️ Tasks: https://paperswithcode.com/task/novel-view-synthesis
@ArtificialIntelligencedl
🔥 Datasets: https://paperswithcode.com/dataset/nerf
@ArtificialIntelligencedl
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STGM: Spatio-Temporal Graph Mixformer for Traffic Forecasting
🖥 Github: https://github.com/Mouradost/STGM
📕 Paper: https://www.sciencedirect.com/science/article/abs/pii/S0957417423007832?via%3Dihub
🔥 Datasets: https://paperswithcode.com/dataset/metr-la
↪️ Tasks: https://paperswithcode.com/task/traffic-prediction
@ArtificialIntelligencedl
🔥 Datasets: https://paperswithcode.com/dataset/metr-la
@ArtificialIntelligencedl
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PerturbScore: Connecting Discrete and Continuous Perturbations in NLP
🖥 Github: https://github.com/renke999/perturbscore
📕 Paper: https://arxiv.org/pdf/2310.08889v1.pdf
🔥 Datasets: https://paperswithcode.com/dataset/imdb-movie-reviews
@ArtificialIntelligencedl
🔥 Datasets: https://paperswithcode.com/dataset/imdb-movie-reviews
@ArtificialIntelligencedl
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Forwarded from Анализ данных (Data analysis)
📒 GigaChat нового поколения.
Разработчики @gigachat_bot изменили подход к обучению модели, а потому практически все умения модели были улучшены. В частности, речь идет о сокращении текстов, ответов на вопросы и генерации идей.
Появился и бот GigaChat в социальной сети «ВКонтакте» — после активации, его можно использовать для самых разных целей: от создания текстов до генерации изображений (за счет интеграции с Kandinsky).
Число уникальных пользователей GigaChat достигло 1 млн.
• Попробовать
@data_analysis_ml
Разработчики @gigachat_bot изменили подход к обучению модели, а потому практически все умения модели были улучшены. В частности, речь идет о сокращении текстов, ответов на вопросы и генерации идей.
Появился и бот GigaChat в социальной сети «ВКонтакте» — после активации, его можно использовать для самых разных целей: от создания текстов до генерации изображений (за счет интеграции с Kandinsky).
Число уникальных пользователей GigaChat достигло 1 млн.
• Попробовать
@data_analysis_ml
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PHA
🖥 Github: https://github.com/bumble666/pha
📕 Paper: https://arxiv.org/pdf/2310.11670v1.pdf
🔥 Datasets: https://paperswithcode.com/dataset/glue
@ArtificialIntelligencedl
cd PHA
pip install -r requirements.txt🔥 Datasets: https://paperswithcode.com/dataset/glue
@ArtificialIntelligencedl
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