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algorithme utiliser
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zjunlp/ModelKinship
공식 코드https://github.com/zjunlp/Prompt4ReasoningPapers
공식 코드https://github.com/zjunlp/PromptKG/tree/main/deltaKG
공식 코드https://github.com/zjunlp/promptkg
공식 코드https://github.com/zjunlp/wkm
공식 코드https://github.com/znowu/cliqueformer-code
공식 코드Cliqueformer: Model-Based Optimization with Structured Transformers
https://github.com/zoukaifeng/4dfm
공식 코드https://github.com/zrrskywalker/llama-adapter
공식 코드LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
https://github.com/zrrskywalker/personalize-sam
공식 코드https://github.com/zurichnlp/swissbert
공식 코드https://github.com/zylipku/SOAD
공식 코드State-observation augmented diffusion model for nonlinear assimilation
https://github.com/zytx121/awesome-vlgfm
공식 코드https://gitlab.com/nct_tso_public/dsad-segmentation
공식 코드One model to use them all: Training a segmentation model with complementary datasets
https://gitlab.com/textpatrol/gp-tp-experiment
공식 코드Shielding Google's language toxicity model against adversarial attacks
https://huggingface.co/AtlaAI/Selene-1-Mini-Llama-3.1-8B
공식 코드https://huggingface.co/utter-project/mHuBERT-147
공식 코드https://worksheets.codalab.org/worksheets/0xad3fc9f52f514e849b282a105b1e3f02
공식 코드Simpler Context-Dependent Logical Forms via Model Projections
https://agit.ai/Polixir/OfflineRL
https://bitbucket.org/paladinanalytics/direct-preference-optimization
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
https://bitbucket.org/taoyds/nbsvm_pos
https://github.com/1-punchMan/CLTS
https://github.com/12kimih/hicupid
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
https://github.com/2023-MindSpore-1/ms-code-15/tree/main/ctcmodel
CTCModel: a Keras Model for Connectionist Temporal Classification
https://github.com/2023-MindSpore-1/ms-code-15/tree/main/wavenet
https://github.com/2023-MindSpore-1/ms-code-151
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-169
https://github.com/2023-MindSpore-1/ms-code-189/tree/main/modelarts
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-195
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-3/tree/main/OctSqueeze
OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression
https://github.com/2023-MindSpore-1/ms-code-34
https://github.com/2023-MindSpore-1/ms-code-58
What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm3
https://github.com/2023-MindSpore-4/Code3/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-4/Code8/tree/main/cnnctc
What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.