Papers with Code 아카이브

Decision Making

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평가표

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보존된 평가 기록

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/mahaitongdae/safety_index_synthesis

Mixed Policy Gradient: off-policy reinforcement learning driven jointly by data and model

https://github.com/mala-lab/anomalygfm

Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements

https://github.com/mangushev/inventory_management

Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains

https://github.com/manujosephv/pytorch_tabular

TabNet: Attentive Interpretable Tabular Learning

https://github.com/marbaga/amf

Active Multi-task Policy Fine-tuning

https://github.com/marciahon29/AlzheimersProject

Transfer Learning with intelligent training data selection for prediction of Alzheimer's Disease

https://github.com/marcinknara/520-ML-DevToolKit

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

https://github.com/mariacer/regression_uncertainty_mm

Uncertainty estimation under model misspecification in neural network regression

https://github.com/markub3327/rl-toolkit

Soft Actor-Critic Algorithms and Applications

https://github.com/marload/DeepRL-TensorFlow2

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/marnixkoops/kendrite

TabNet: Attentive Interpretable Tabular Learning

https://github.com/massquantity/DBRL

Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

https://github.com/mateoespinosa/concept-quality

Promises and Pitfalls of Black-Box Concept Learning Models

https://github.com/materialsvirtuallab/matgl

Relational inductive biases, deep learning, and graph networks

https://github.com/materialsvirtuallab/megnet

Relational inductive biases, deep learning, and graph networks

https://github.com/matheushent/score-cam

Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

https://github.com/mathiasunberath/DeepDRR

X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery

https://github.com/matteo-rizzo/faithful-attention-eval

Evaluating the Faithfulness of Saliency-based Explanations for Deep Learning Models for Temporal Colour Constancy

https://github.com/maudzung/TTNet-Realtime-for-Table-Tennis-Pytorch

TTNet: Real-time temporal and spatial video analysis of table tennis

https://github.com/maxiaoba/SoftLearning

Soft Actor-Critic Algorithms and Applications

https://github.com/maxqyzhu/TS_for_mean_variance_bandit

Thompson Sampling Algorithms for Mean-Variance Bandits

https://github.com/mbhenaff/EEN

Query-Efficient Imitation Learning for End-to-End Autonomous Driving

https://github.com/mbilalzafar/fair-classification

Fairness Constraints: Mechanisms for Fair Classification

https://github.com/mengyuest/reactive_cbf

Reactive and Safe Road User Simulations using Neural Barrier Certificates

https://github.com/metadriverse/aco

Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy Pretraining

https://github.com/mgbellemare/Arcade-Learning-Environment

The Arcade Learning Environment: An Evaluation Platform for General Agents

https://github.com/mgrankin/fast_tabnet

TabNet: Attentive Interpretable Tabular Learning

https://github.com/miaoruonan/MACA_test

Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

https://github.com/michaelkapteyn/UAV-Digital-Twin

A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale

https://github.com/michaelkapteyn/UAV-Experimental-Calibration

A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale

https://github.com/michaelwan11/MIKT

Mutual Information Based Knowledge Transfer Under State-Action Dimension Mismatch

https://github.com/michal-crosta/GIT19

The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

https://github.com/michalnand/reinforcement_learning

Self-supervised network distillation: an effective approach to exploration in sparse reward environments

https://github.com/microsoft/cameratraps

A deep active learning system for species identification and counting in camera trap images

https://github.com/microsoft/csrobust

Time-uniform confidence bands for the CDF under nonstationarity

https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/pytorch_tabnet.py

TabNet: Attentive Interpretable Tabular Learning

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