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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/ncclab-sustech/cocog
공식 코드CoCoG: Controllable Visual Stimuli Generation based on Human Concept Representations
https://github.com/ncclab-sustech/cocog-2
공식 코드CoCoG-2: Controllable generation of visual stimuli for understanding human concept representation
https://github.com/nch08a/EDVizPhenotyping
공식 코드Visualization of Emergency Department Clinical Data for Interpretable Patient Phenotyping
https://github.com/ndexter/MLFA
공식 코드The gap between theory and practice in function approximation with deep neural networks
https://github.com/neheller/kits19
공식 코드https://github.com/neildhir/dcbo
공식 코드https://github.com/nekkar/interactive_training
공식 코드Towards Interactive Training of Non-Player Characters in Video Games
https://github.com/neonnnnn/torchbcsoftmax
공식 코드Box-Constrained Softmax Function and Its Application for Post-Hoc Calibration
https://github.com/netpaladinx/NeuCFlow
공식 코드https://github.com/networks-learning/counterfactual-continuous-mdp
공식 코드Finding Counterfactually Optimal Action Sequences in Continuous State Spaces
https://github.com/networks-learning/counterfactual-explanations-mdp
공식 코드Counterfactual Explanations in Sequential Decision Making Under Uncertainty
https://github.com/networks-learning/human-aligned-calibration
공식 코드https://github.com/networks-learning/human-alignment-study
공식 코드Human-Alignment Influences the Utility of AI-assisted Decision Making
https://github.com/neu-spiral/rbc-satimg
공식 코드Recursive classification of satellite imaging time-series: An application to land cover mapping
https://github.com/neuhai/explanation-enriched-active-learning
공식 코드https://github.com/neulab/incremental_tree_edit
공식 코드Learning Structural Edits via Incremental Tree Transformations
https://github.com/neurai-lab/mt-sfmlearner
공식 코드https://github.com/newlei/fda
공식 코드https://github.com/nhashemi202/ICMO
공식 코드Inductive Biases for Zero-shot Systematic Generalization in Language-informed Reinforcement Learning
https://github.com/nick-jhlee/bandits-network
공식 코드Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex Networks
https://github.com/nickkantack/Hanabi-Cyclone
공식 코드Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI Teammates
https://github.com/nickkunz/idling
공식 코드Global Geolocated Realtime Data of Interfleet Urban Transit Bus Idling
https://github.com/nicolamarinello/offsetocc
공식 코드Camera-Only 3D Panoptic Scene Completion for Autonomous Driving through Differentiable Object Shapes
https://github.com/nicomeyer96/ws-varqpi
공식 코드https://github.com/niftynans/climax
공식 코드CLIMAX: An exploration of Classifier-Based Contrastive Explanations
https://github.com/nikhilroxtomar/DDANet
공식 코드DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation
https://github.com/nikikilbertus/fair-decisions
공식 코드https://github.com/niklasfreymuth/vigor
공식 코드Inferring Versatile Behavior from Demonstrations by Matching Geometric Descriptors
https://github.com/nikskiks/learning-reward-function-demand-response
공식 코드Learning a Reward Function for User-Preferred Appliance Scheduling
https://github.com/nikunj-gupta/conformal-agent-modelling
공식 코드CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learning
https://github.com/nikunj-gupta/on-the-calibration-of-compartmental-epidemiological-models
공식 코드https://github.com/nilsleh/tropical_cyclone_uq
공식 코드Uncertainty Aware Tropical Cyclone Wind Speed Estimation from Satellite Data
https://github.com/ninarehm/attribution
공식 코드https://github.com/ningwang2049/laiar
공식 코드https://github.com/niru-umass-dev/caspr
공식 코드CASPR: Automated Evaluation Metric for Contrastive Summarization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.