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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/thuml/iVideoGPT

공식 코드

iVideoGPT: Interactive VideoGPTs are Scalable World Models

https://github.com/tianjunz/hir

공식 코드

The Wisdom of Hindsight Makes Language Models Better Instruction Followers

https://github.com/tianxiangzhao/imitationnoisydemon

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Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations

https://github.com/tianxusky/tabular-ail

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Provably Efficient Adversarial Imitation Learning with Unknown Transitions

https://github.com/tiehangd/ue-eeg

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Uncertainty Detection and Reduction in Neural Decoding of EEG Signals

https://github.com/tijana-zrnic/cross-ppi

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Cross-Prediction-Powered Inference

https://github.com/tingstat/Data_Combination

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Combining Experimental and Historical Data for Policy Evaluation

https://github.com/tinker-twins/autorace-simulator

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Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture

https://github.com/tkipf/compile

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CompILE: Compositional Imitation Learning and Execution

https://github.com/tkusmierczyk/lcvi

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Variational Bayesian Decision-making for Continuous Utilities

https://github.com/tlabarta/helpfulnessofxai

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Study on the Helpfulness of Explainable Artificial Intelligence

https://github.com/tleemann/protectedconsent

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I Prefer not to Say: Protecting User Consent in Models with Optional Personal Data

https://github.com/to-mi/pttb

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Probabilistic Formulation of the Take The Best Heuristic

https://github.com/tobeatraceur/Organized-LLM-Agents

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Embodied LLM Agents Learn to Cooperate in Organized Teams

https://github.com/tobhatt/SeqDeconf

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Sequential Deconfounding for Causal Inference with Unobserved Confounders

https://github.com/tobhatt/donut

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Estimating Average Treatment Effects via Orthogonal Regularization

https://github.com/tobyleelsz/iqmpc

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Reward-free World Models for Online Imitation Learning

https://github.com/tochris/falcon

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Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration

https://github.com/tomekkorbak/pretraining-with-human-feedback

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Pretraining Language Models with Human Preferences

https://github.com/tomkingsforduoa/social-reward-function

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A General, Evolution-Inspired Reward Function for Social Robotics

https://github.com/tommasomarzi/fgrl

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Feudal Graph Reinforcement Learning

https://github.com/tomsilver/pddlgym

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PDDLGym: Gym Environments from PDDL Problems

https://github.com/tomyot/measurement-simplification

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Measurement Simplification in ρ-POMDP with Performance Guarantees

https://github.com/tongji-kgllm/industryscope

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Decoding Urban Industrial Complexity: Enhancing Knowledge-Driven Insights via IndustryScopeGPT

https://github.com/tongjifinlab/cfgpt

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CFGPT: Chinese Financial Assistant with Large Language Model

https://github.com/tongnie/nexusqn

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Contextualizing MLP-Mixers Spatiotemporally for Urban Data Forecast at Scale

https://github.com/torylin/sentecon

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SenteCon: Leveraging Lexicons to Learn Human-Interpretable Language Representations

https://github.com/toshihiro-ota/decision-mamba

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Decision Mamba: Reinforcement Learning via Sequence Modeling with Selective State Spaces

https://github.com/toygarr/counterfactual-explanations-for-medical-research

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Beyond Known Reality: Exploiting Counterfactual Explanations for Medical Research

https://github.com/toytiny/milliflow

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milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing

https://github.com/traffic-alpha/llm-assisted-light

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LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments

https://github.com/trinhvg/videopath-llava

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VideoPath-LLaVA: Pathology Diagnostic Reasoning Through Video Instruction Tuning

https://github.com/trotsky1997/mathblackbox

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Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B

https://github.com/trsav/hitl-bo

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Expert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known Systems

https://github.com/trust-nlp/coi-psychotherapy

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Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts

https://github.com/tshi04/ACCE

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Corpus-level and Concept-based Explanations for Interpretable Document Classification

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