https://github.com/simone-bombari/spurious-features-memorization
공식 코드How Spurious Features Are Memorized: Precise Analysis for Random and NTK Features
Papers with Code 아카이브
Learning Theory
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Learning theory
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/skander-karkar/LAP
공식 코드A Principle of Least Action for the Training of Neural Networks
https://github.com/sophiaas/spectral-universality
공식 코드Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks
https://github.com/stellatogrp/datadrivenoptimizerguarantees
공식 코드Data-Driven Performance Guarantees for Classical and Learned Optimizers
https://github.com/susanwe/RLCT
공식 코드https://github.com/suswei/bnn_via_slt
공식 코드Variational Bayesian Neural Networks via Resolution of Singularities
https://github.com/sylydya/Consistent-Sparse-Deep-Learning-Theory-and-Computation
공식 코드https://github.com/tml-epfl/why-weight-decay
공식 코드https://github.com/tmlr-group/dal
공식 코드Learning to Augment Distributions for Out-of-Distribution Detection
https://github.com/tolgabirdal/phdimgeneralization
공식 코드Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks
https://github.com/tomwaka/ba-overparameterized-nonlinreg
공식 코드Bayesian Inference for Consistent Predictions in Overparameterized Nonlinear Regression
https://github.com/vihan-lakshman/diminishing-returns-wide-continual-learning
공식 코드https://github.com/xjtushujun/slem-theory
공식 코드Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks
https://github.com/xu-ji/information-bottleneck
공식 코드https://github.com/zahraatashgahi/ctre
공식 코드A Brain-inspired Algorithm for Training Highly Sparse Neural Networks
https://github.com/zhangyk8/DirMS
공식 코드Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data
https://github.com/Andy-TK/COMP90051_Project1
A Contextual-Bandit Approach to Personalized News Article Recommendation
https://github.com/DavidsonAle/Robust-Learning---Effects-of-Mutual-Information-Penalization
https://github.com/EduardOravkin/optimal_response_linear_interpolator
https://github.com/EduardOravkin/optimal_rla_interpolator
https://github.com/MindCode-4/code-12/tree/main/learning-an-explicit-hyperparameter
Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks
https://github.com/TenglunTan/Multi-armed-bandits
A Contextual-Bandit Approach to Personalized News Article Recommendation
https://github.com/UnknownUnit/Bandits_project
A Contextual-Bandit Approach to Personalized News Article Recommendation
https://github.com/akhadangi/Multi-armed-Bandits
A Contextual-Bandit Approach to Personalized News Article Recommendation
https://github.com/andertavares/mlbf
https://github.com/antonismand/Personalized-News-Recommendation
A Contextual-Bandit Approach to Personalized News Article Recommendation
https://github.com/danilprov/batch-bandits
A Contextual-Bandit Approach to Personalized News Article Recommendation
https://github.com/facebookresearch/ReAgent
A Contextual-Bandit Approach to Personalized News Article Recommendation
https://github.com/fournierlouis/synaptic_sampling_rbm
https://github.com/fraziezr/Machine_Learning_Final_Project_Team_12
A Bayesian Perspective of Statistical Machine Learning for Big Data
https://github.com/hwalinga/FSM-learning
https://github.com/hzlbbfrog/generative-bim
DDIM sampling for Generative AIBIM, a faster intelligent structural design framework
https://github.com/informagi/AQA
A Contextual-Bandit Approach to Personalized News Article Recommendation
https://github.com/jonhue/activeft
https://github.com/jonhue/transductive-active-learning
https://github.com/leoandeol/cods
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.