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Decision Making
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mtoles/BeNYfits-ProADA
공식 코드Program Synthesis Dialog Agents for Interactive Decision-Making
https://github.com/mugekural9/cogeval
공식 코드Quantifying Divergence for Human-AI Collaboration and Cognitive Trust
https://github.com/murinj/big-moe
공식 코드BIG-MoE: Bypass Isolated Gating MoE for Generalized Multimodal Face Anti-Spoofing
https://github.com/murongyue/llm_mot_cascade
공식 코드Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning
https://github.com/murpheylab/maxdiffrl
공식 코드https://github.com/musicalOffering/ActionSwitch-release
공식 코드ActionSwitch: Class-agnostic Detection of Simultaneous Actions in Streaming Videos
https://github.com/musslick/DARTS-Cognitive-Modeling
공식 코드https://github.com/mustafakarabag/llmchameleon
공식 코드https://github.com/mutschcr/c-mcts
공식 코드https://github.com/mvandenhi/p2p
공식 코드From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection
https://github.com/mvandenhi/signal-is-harder
공식 코드Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss
https://github.com/mwolff31/signal_preference
공식 코드Signal Strength and Noise Drive Feature Preference in CNN Image Classifiers
https://github.com/mxu34/mbrl-gpmm
공식 코드Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
https://github.com/my-name-is-D/high_level_nav_planning
공식 코드https://github.com/myeon9h/planrag
공식 코드https://github.com/mygithth27/af-risk-prediction-by-ecg-dnn
공식 코드End-to-end Risk Prediction of Atrial Fibrillation from the 12-Lead ECG by Deep Neural Networks
https://github.com/mylad13/catmip
공식 코드https://github.com/myusha/rankings_and_quantized_scores
공식 코드https://github.com/mzaffran/adaptiveconformalpredictionstimeseries
공식 코드https://github.com/n-mehandru/physicianqe
공식 코드https://github.com/nacharki/multiplet-metalearners
공식 코드Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects
https://github.com/nafissadeq/reasoning-distillation
공식 코드https://github.com/namkoong-lab/inductive-biases-exchangeable-sequence
공식 코드Architectural and Inferential Inductive Biases For Exchangeable Sequence Modeling
https://github.com/namnguyen0510/EtaNet
공식 코드Translational Quantum Machine Intelligence for Modeling Tumor Dynamics in Oncology
https://github.com/nandomp/missingFairness
공식 코드https://github.com/naslabbgu/rplace-engagement-prediction
공식 코드https://github.com/nathangavenski/il-datasets
공식 코드Imitation Learning Datasets: A Toolkit For Creating Datasets, Training Agents and Benchmarking
https://github.com/nathangavenski/sail
공식 코드https://github.com/nathankallus/universalsprt
공식 코드https://github.com/natliblux/nautilusocr
공식 코드Rerunning OCR: A Machine Learning Approach to Quality Assessment and Enhancement Prediction
https://github.com/navysanghvi/MGpi
공식 코드MGpi: A Computational Model of Multiagent Group Perception and Interaction
https://github.com/nazaninmn/mosare
공식 코드Towards Robust Multimodal Representation: A Unified Approach with Adaptive Experts and Alignment
https://github.com/nazat28/xai-based-feature-ensemble-for-enhanced-anomaly-detection-in-autonomous-driving-systems
공식 코드XAI-based Feature Ensemble for Enhanced Anomaly Detection in Autonomous Driving Systems
https://github.com/nazeehgh/CostSensetive_Telemarketing_MOGA
공식 코드https://github.com/nbalepur/mcqa-artifacts
공식 코드Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.