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Learning theory
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Anjin-Liu/Openset_Learning_AOSR
공식 코드https://github.com/CogComp/PABI
공식 코드https://github.com/DeepLearningSaeid/New-Type-of-Deep-Learning
공식 코드https://github.com/HornHehhf/PABI
공식 코드https://github.com/IBM/geostats-gen-error
공식 코드https://github.com/Learning-and-Intelligent-Systems/Analytical-Learning-Theory
공식 코드Generalization in Machine Learning via Analytical Learning Theory
https://github.com/Learning-and-Intelligent-Systems/DualCutout
공식 코드Generalization in Machine Learning via Analytical Learning Theory
https://github.com/Nash-Lab/DiscontinuousLCs
공식 코드https://github.com/NikolaKon1994/Robust-Learning-from-Untrusted-Sources
공식 코드https://github.com/PanBikang/PromptFolio
공식 코드Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method
https://github.com/Pehlevan-Group/sgd_structured_features
공식 코드https://github.com/SeanJia/InfoMCR
공식 코드Information-Theoretic Local Minima Characterization and Regularization
https://github.com/ZhangXiao96/OptimizationVariance
공식 코드Optimization Variance: Exploring Generalization Properties of DNNs
https://github.com/ZhangXiao96/RethinkSpectralBias
공식 코드Rethink the Connections among Generalization, Memorization and the Spectral Bias of DNNs
https://github.com/aangelopoulos/gradient-equilibrium
공식 코드Gradient Equilibrium in Online Learning: Theory and Applications
https://github.com/alexfarhang/margin
공식 코드Investigating Generalization by Controlling Normalized Margin
https://github.com/algue-rythme/distributionregressionus2016
공식 코드Improved learning theory for kernel distribution regression with two-stage sampling
https://github.com/amazon-research/causal-forecasting
공식 코드Causal Forecasting:Generalization Bounds for Autoregressive Models
https://github.com/andrewyyp/kernel-doubly-robust
공식 코드https://github.com/anthonysicilia/modeling-non-cooperation-tacl2022
공식 코드Modeling Non-Cooperative Dialogue: Theoretical and Empirical Insights
https://github.com/antonvalk/modl
공식 코드https://github.com/arjunsubramonian/auto-fpt
공식 코드auto-fpt: Automating Free Probability Theory Calculations for Machine Learning Theory
https://github.com/ars22/meta-learning-eval-id-vs-ood
공식 코드Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution
https://github.com/ashesh6810/fourks
공식 코드https://github.com/ativjoshi/universalcaching
공식 코드https://github.com/barrett-python/cpal
공식 코드Hunting Attributes: Context Prototype-Aware Learning for Weakly Supervised Semantic Segmentation
https://github.com/benjidupuis/data_dependent_dimensions
공식 코드Generalization Bounds with Data-dependent Fractal Dimensions
https://github.com/bhanML/label-noise-papers
공식 코드A Survey of Label-noise Representation Learning: Past, Present and Future
https://github.com/bnkausik/occam_gradient_descent
공식 코드https://github.com/bpcarlos/double_descent_quantum_ml
공식 코드https://github.com/calvinyeungck/Football-1-vs-1-Shot-Taking-Situations-Analysis
공식 코드https://github.com/calvinyeungck/analyzing-two-agents-interaction-in-football-shot-taking-situations
공식 코드https://github.com/carlossantosgarcia/pnp_lantern
공식 코드A primal-dual data-driven method for computational optical imaging with a photonic lantern
https://github.com/cea-list/metamtreg
공식 코드Improving Few-Shot Learning through Multi-task Representation Learning Theory
https://github.com/charliebtan/fractal_dimensions
공식 코드On the Limitations of Fractal Dimension as a Measure of Generalization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.