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Decision Making

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

https://github.com/snowteam2023/medrag

공식 코드

A Smart Multimodal Healthcare Copilot with Powerful LLM Reasoning

https://github.com/snurkabill/MasterThesis

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Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes

https://github.com/soda-inria/causal_ehr_mimic

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Causal thinking for decision making on Electronic Health Records: why and how

https://github.com/soerenab/AudioMNIST

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

https://github.com/sohaibayub/camp

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CAMP: A Context-Aware Cricket Players Performance Metric

https://github.com/som-shahlab/fairness_benchmark

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An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction

https://github.com/som-shahlab/net_benefit_ascvd

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Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare

https://github.com/somewordstoolate/quicker

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From Questions to Clinical Recommendations: Large Language Models Driving Evidence-Based Clinical Decision Making

https://github.com/somritabanerjee/bimdps.jl

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Contingency Planning Using Bi-level Markov Decision Processes for Space Missions

https://github.com/somsubhra04/dravlangtech_ai-gen-prod-rev

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Unmask It! AI-Generated Product Review Detection in Dravidian Languages

https://github.com/songhui01/csm-h-r

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CSM-H-R: A Context Modeling Framework in Supporting Reasoning Automation for Interoperable Intelligent Systems and Privacy Protection

https://github.com/sony/okg

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OKG: On-the-Fly Keyword Generation in Sponsored Search Advertising

https://github.com/sonyai/a_view_from_somewhere

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A View From Somewhere: Human-Centric Face Representations

https://github.com/sophiagu/RLF

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Deep Reinforcement Learning with Function Properties in Mean Reversion Strategies

https://github.com/sophiejaffard/mel

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Spiking Neural Models for Decision-Making Tasks with Learning

https://github.com/sorensc/adesse

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ADESSE: Advice Explanations in Complex Repeated Decision-Making Environments

https://github.com/soroush-bn/ProtoX

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ProtoX: Explaining a Reinforcement Learning Agent via Prototyping

https://github.com/sotif-avlab/pesotif

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SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving

https://github.com/soumickmj/GPModels

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Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification

https://github.com/soumickmj/pulaski

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PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation

https://github.com/soyeonm/film

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FILM: Following Instructions in Language with Modular Methods

https://github.com/soyeonm/teach_film

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Don't Copy the Teacher: Data and Model Challenges in Embodied Dialogue

https://github.com/spiral-med/ophiuchus

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O1 Replication Journey -- Part 3: Inference-time Scaling for Medical Reasoning

https://github.com/spl-bgu/polyplan

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Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex Tasks

https://github.com/spotify-research/il-by-rl

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Imitation Learning by Reinforcement Learning

https://github.com/spring-epfl/pots

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POTs: Protective Optimization Technologies

https://github.com/sreerammarimuthu/table-unionability-study

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Humans, Machine Learning, and Language Models in Union: A Cognitive Study on Table Unionability

https://github.com/sriram94/multiagentadvisorqlearning

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Multi-Agent Advisor Q-Learning

https://github.com/srishtigautam/kmex

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Prototypical Self-Explainable Models Without Re-training

https://github.com/ssg-research/flare

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FLARE: Fingerprinting Deep Reinforcement Learning Agents using Universal Adversarial Masks

https://github.com/ssmisya/PRMBench

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PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models

https://github.com/staciiaz/cog2023-darefightingice-evaluation

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Achieving Fairness in DareFightingICE Agents Evaluation Through a Delay Mechanism

https://github.com/stanford-iliad/ileed

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Imitation Learning by Estimating Expertise of Demonstrators

https://github.com/stanford-policylab/learning-to-be-fair

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Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making

https://github.com/stanfordai4hi/poela

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Offline Policy Optimization with Eligible Actions

https://github.com/stanfordhci/modelsketchbook

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Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design

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