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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/james-simon/reverse-engineering

공식 코드

Reverse Engineering the Neural Tangent Kernel

https://github.com/james-simon/shallow-learning

공식 코드

Reverse Engineering the Neural Tangent Kernel

https://github.com/janzing/janzing.github.io

공식 코드

Causal Regularization

https://github.com/joostPieterse/CosineSET

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A Brain-inspired Algorithm for Training Highly Sparse Neural Networks

https://github.com/jose3g/Learning_CHARME_models_with_DNN

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Learning CHARME models with neural networks

https://github.com/juliusberner/theory2practice

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Learning ReLU networks to high uniform accuracy is intractable

https://github.com/khammernik/sigmanet

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$Σ$-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction

https://github.com/kwignb/randomneuralfield

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Deep Learning in Random Neural Fields: Numerical Experiments via Neural Tangent Kernel

https://github.com/lawliet-zzl/gl

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Gray Learning from Non-IID Data with Out-of-distribution Samples

https://github.com/leakdetectai/automlquantildetect

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Information Leakage Detection through Approximate Bayes-optimal Prediction

https://github.com/leungsamwai/nn4manifoldpde

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Solving PDEs on Unknown Manifolds with Machine Learning

https://github.com/litian96/TERM

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On Tilted Losses in Machine Learning: Theory and Applications

https://github.com/lopezpaz/causation_learning_theory

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Towards a Learning Theory of Cause-Effect Inference

https://github.com/lywang3081/Awesome-Continual-Learning

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A Comprehensive Survey of Continual Learning: Theory, Method and Application

https://github.com/machine-reasoning-ufrgs/mlbf

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Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic Models

https://github.com/matt00n/policygradientsjax

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The Definitive Guide to Policy Gradients in Deep Reinforcement Learning: Theory, Algorithms and Implementations

https://github.com/mattclifford1/confidence-bounds-ecai24

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Learning Confidence Bounds for Classification with Imbalanced Data

https://github.com/mizvol/WikiBrain

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Wikipedia graph mining: dynamic structure of collective memory

https://github.com/nixay/causal-stonet

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Causal-StoNet: Causal Inference for High-Dimensional Complex Data

https://github.com/paulviallard/aistats24-complexity-measures

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Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures

https://github.com/pbelcak/neregrain

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A Neural Model for Regular Grammar Induction

https://github.com/pietronvll/DPNets

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Learning invariant representations of time-homogeneous stochastic dynamical systems

https://github.com/pietronvll/encoderops

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Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems

https://github.com/qunovo/NLPrompt

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NLPrompt: Noise-Label Prompt Learning for Vision-Language Models

https://github.com/rahul13ramesh/modelzoo_continual

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Model Zoo: A Growing "Brain" That Learns Continually

https://github.com/rekino/analytic_robustness

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An Analytic Framework for Robust Training of Artificial Neural Networks

https://github.com/rodsveiga/mc_perceptron

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Learning curves for the multi-class teacher-student perceptron

https://github.com/rodsveiga/sgf_dyn

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Stochastic Gradient Flow Dynamics of Test Risk and its Exact Solution for Weak Features

https://github.com/rotskoff-group/learning-committor

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Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalization

https://github.com/rpatrik96/llm-non-identifiability

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Position: Understanding LLMs Requires More Than Statistical Generalization

https://github.com/rylanschaeffer/stanford-ai-alignment-double-descent-tutorial

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Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle

https://github.com/ryoma-k/llpiwc

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Learning from Label Proportions with Instance-wise Consistency

https://github.com/sangdon/PAC-confidence-set

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PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction

https://github.com/satoshi-hayakawa/kernel-quadrature

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Sampling-based Nyström Approximation and Kernel Quadrature

https://github.com/shenzebang/SRPFL

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Straggler-Resilient Personalized Federated Learning

https://github.com/sherbret/nl-ridge

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Towards a unified view of unsupervised non-local methods for image denoising: the NL-Ridge approach

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.