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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/sisl/SCoBA.jl

공식 코드

Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints

https://github.com/sisl/action_suggestions

공식 코드

Collaborative Decision Making Using Action Suggestions

https://github.com/sisl/bayesiansafetyvalidation.jl

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Bayesian Safety Validation for Failure Probability Estimation of Black-Box Systems

https://github.com/sisl/betazero.jl

공식 코드

BetaZero: Belief-State Planning for Long-Horizon POMDPs using Learned Approximations

https://github.com/sisl/cpomcpplus

공식 코드

Addressing Myopic Constrained POMDP Planning with Recursive Dual Ascent

https://github.com/sisl/delsmm

공식 코드

Training Structured Mechanical Models by Minimizing Discrete Euler-Lagrange Residual

https://github.com/sisl/evacuationpomdp.jl

공식 코드

Prioritizing emergency evacuations under compounding levels of uncertainty

https://github.com/sisl/gail-driver

공식 코드

Imitating Driver Behavior with Generative Adversarial Networks

https://github.com/sisl/jointdemandforecasting

공식 코드

Conditional Approximate Normalizing Flows for Joint Multi-Step Probabilistic Forecasting with Application to Electricity Demand

https://github.com/sisl/ngsim_env

공식 코드

Multi-Agent Imitation Learning for Driving Simulation

https://github.com/sisl/ventilators

공식 코드

Optimal Control of Mechanical Ventilators with Learned Respiratory Dynamics

https://github.com/siyan-zhao/decision-stacks

공식 코드

Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models

https://github.com/sjauhri/Interactive-Learning-in-State-space

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Interactive Imitation Learning in State-Space

https://github.com/sjgershm/exploration

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Comparing Exploration-Exploitation Strategies of LLMs and Humans: Insights from Standard Multi-armed Bandit Tasks

https://github.com/sjiang87/deephyper

공식 코드

Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search

https://github.com/sjtuytc/-AAAI21-RoutineAugmentedPolicyLearning-RAPL-

공식 코드

Augmenting Policy Learning with Routines Discovered from a Single Demonstration

https://github.com/sjtuytc/AAAI21-RoutineAugmentedPolicyLearning

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Augmenting Policy Learning with Routines Discovered from a Single Demonstration

https://github.com/sjtuytc/Neurips21-ProTo-Program-guided-Transformers-for-Program-guided-Tasks

공식 코드

ProTo: Program-Guided Transformer for Program-Guided Tasks

https://github.com/sjunges/shielding-POMDPs

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Enforcing Almost-Sure Reachability in POMDPs

https://github.com/sjyucnel/conditional_cs_divergence

공식 코드

The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making

https://github.com/skandavaidyanath/credit-assignment

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Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning

https://github.com/skywalkerzhang/Defeasible_Visual_Entailment

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Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven Optimization

https://github.com/slab-nlp/cog-gb-eval

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Comparing Humans and Models on a Similar Scale: Towards Cognitive Gender Bias Evaluation in Coreference Resolution

https://github.com/sldeng1003/gild

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Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline Data

https://github.com/slds-lmu/paper_2023_cfml

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Causal Fair Machine Learning via Rank-Preserving Interventional Distributions

https://github.com/sled-group/watch-talk-and-guide

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Can Foundation Models Watch, Talk and Guide You Step by Step to Make a Cake?

https://github.com/sleepymalc/Travel-the-Same-Path

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Travel the Same Path: A Novel TSP Solving Strategy

https://github.com/slundberg/shap

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ViralVectors: Compact and Scalable Alignment-free Virome Feature Generation

https://github.com/sluxsr/goat

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GOAt: Explaining Graph Neural Networks via Graph Output Attribution

https://github.com/smallporridge/assistrag

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AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information Assistant

https://github.com/snakeztc/NeuralDialog-CVAE

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Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders

https://github.com/snakeztc/NeuralDialog-LaRL

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Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models

https://github.com/snap-stanford/POPPER

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Automated Hypothesis Validation with Agentic Sequential Falsifications

https://github.com/snap-stanford/mlagentbench

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MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation

https://github.com/snnzhao/dahcr

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Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning

https://github.com/snowflake-labs/checkboxqa

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Unchecked and Overlooked: Addressing the Checkbox Blind Spot in Large Language Models with CheckboxQA

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