https://github.com/MindSpore-scientific/code-14/tree/main/stable-sam
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound
Papers with Code 아카이브
Open-Ended Question Answering
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Open-ended questions are defined as those that simply pose the question, without imposing any constraints on the format of the response. This distinguishes them from questions with a predetermined answer format.
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Mingyuan-Zhu/BCI_practice
Deep learning-based electroencephalography analysis: a systematic review
https://github.com/OPTAMI/OPTAMI
The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization
https://github.com/Oichii/resnet3D_pulse
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/POLane16/DIAG
Stochastic subgradient method converges at the rate $O(k^{-1/4})$ on weakly convex functions
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/Raghuvar/pneumonia_detection_using_X-rays
https://github.com/SGNovice/Disease-detection-using-chest-xrays
https://github.com/Shito0907/3D_resnet
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/TRKuan/cxr8
https://github.com/TheInfamousWayne/CheXNet
https://github.com/Tsinghua-MARS-Lab/M2I
M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction
https://github.com/Valentyn1997/semi-supervised-text-classification
https://github.com/VinAIResearch/PCC-pytorch
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control
https://github.com/Wenlin-Chen/ADKF-IFT
https://github.com/aaaasssddf/word-embedding-dimensionality-selection
https://github.com/aimagelab/mammoth
SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model
https://github.com/alewarne/ExplainSecurityDNNs
Evaluating Explanation Methods for Deep Learning in Security
https://github.com/alewarne/Layerwise-Relevance-Propagation-for-LSTMs
Evaluating Explanation Methods for Deep Learning in Security
https://github.com/alinstein/X_RAY
https://github.com/amirassov/kaggle-imaterialist
https://github.com/andrewzhe/lawyer-llama
Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering
https://github.com/arnoweng/CheXNet
https://github.com/asap7772/fewshot-preference-optimization
https://github.com/bennati/baldwin_veering
https://github.com/bytetriper/lm4lv
https://github.com/caihuaiguang/CHG-Shapley-for-Data-Selection
Coresets for Data-efficient Training of Machine Learning Models
https://github.com/cdmdc/lr-identify-custom
https://github.com/chail/latent-composition
Using latent space regression to analyze and leverage compositionality in GANs
https://github.com/dahong67/ConvolutionalAnalysisOperatorLearning.jl
Convolutional Analysis Operator Learning: Dependence on Training Data
https://github.com/dahong67/ConvolutionalOperatorLearning.jl
Convolutional Analysis Operator Learning: Dependence on Training Data
https://github.com/decile-team/cords
Coresets for Data-efficient Training of Machine Learning Models
https://github.com/dmis-lab/olaph
https://github.com/dodo47/SpikE
https://github.com/dongquan11/SOPPP_CB_and_HS_games
Path Planning Problems with Side Observations-When Colonels Play Hide-and-Seek
https://github.com/dwiggles/AIX360-withdata
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.