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Decision Making

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코드

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

https://github.com/rperezdattari/Interactive-Learning-with-Corrective-Feedback-for-Policies-based-on-Deep-Neural-Networks

Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks

https://github.com/rrags/ProtoX_NeurIPS

ProtoX: Explaining a Reinforcement Learning Agent via Prototyping

https://github.com/ruixinhua/explainednrs

A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification

https://github.com/rwightman/pytorch-opensim-rl

Self-Imitation Learning

https://github.com/s-gupta/map-plan-baseline

Learning Exploration Policies for Navigation

https://github.com/safe-rl-team/viper-verifiable-rl-impl

Verifiable Reinforcement Learning via Policy Extraction

https://github.com/sai4july/custext

MedSTS: A Resource for Clinical Semantic Textual Similarity

https://github.com/saipradeep-peri/Neural_Additive_Models_Walkthrough

Neural Additive Models: Interpretable Machine Learning with Neural Nets

https://github.com/saitcakmak/contextual_rs

Bayesian Optimization of Risk Measures

https://github.com/salesforce/ai-economist

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

https://github.com/salesforceairesearch/agentlite

BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents

https://github.com/salmank255/ROAD_Waymo_Baseline

ROAD: The ROad event Awareness Dataset for Autonomous Driving

https://github.com/salvaRC/graphnet_pytorch

Relational inductive biases, deep learning, and graph networks

https://github.com/samuel500/World-Models

Deep Neuroevolution of Recurrent and Discrete World Models

https://github.com/sanchit97/secl

A Self-explaining Neural Architecture for Generalizable Concept Learning

https://github.com/sb-ai-lab/RePlay

Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling

https://github.com/sb-ai-lab/sketchboost-paper

TabNet: Attentive Interpretable Tabular Learning

https://github.com/scicompmod/memilio

Graph Neural Network Surrogates to leverage Mechanistic Expert Knowledge towards Reliable and Immediate Pandemic Response

https://github.com/scikit-learn-contrib/mapie

Distribution-Free, Risk-Controlling Prediction Sets

https://github.com/scottemmons/youngs-cql

Soft Actor-Critic Algorithms and Applications

https://github.com/sebastian-lapuschkin/explaining-deep-clinical-gait-classification

On the Explanation of Machine Learning Predictions in Clinical Gait Analysis

https://github.com/sebastian-lapuschkin/interpretable-deep-gait-injury

On the Explanation of Machine Learning Predictions in Clinical Gait Analysis

https://github.com/securade/hub

BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis

https://github.com/sentient-engineering/agent-q

WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

https://github.com/seohongpark/ogbench

Is Value Learning Really the Main Bottleneck in Offline RL?

https://github.com/servicenow/beyond-trivial-explanations

Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations

https://github.com/seungju-k1m/sac-td3-td7

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

https://github.com/shadowyzy/NIPS2019-MineRL-Competition-solution

The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors

https://github.com/shahink84/OnlineSearchRBIS

Online Search With Best-Price and Query-Based Predictions

https://github.com/shai128/rrc

Adaptive Conformal Predictions for Time Series

https://github.com/shalabh147/Brain-Tumor-Segmentation-and-Survival-Prediction-using-Deep-Neural-Networks

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

https://github.com/shalijiang/bo

Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees

https://github.com/shamanez/Variational-Discriminator-Bottleneck-Tensorflow-Implementation

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

https://github.com/shashist/recsys-rl

Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling

https://github.com/shekharRavi/ask-no-more-COLING2018

Ask No More: Deciding when to guess in referential visual dialogue

https://github.com/shenchenyi/SpeakerData

AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark

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