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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/valdecy/pybibx

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pyBibX -- A Python Library for Bibliometric and Scientometric Analysis Powered with Artificial Intelligence Tools

https://github.com/valeoai/VideoActionModel

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VaViM and VaVAM: Autonomous Driving through Video Generative Modeling

https://github.com/valeoai/WoodScape/tree/master/omnidet

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Surround-View Cameras based Holistic Visual Perception for Automated Driving

https://github.com/valeoai/v-max

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V-Max: A Reinforcement Learning Framework for Autonomous Driving

https://github.com/valeriekrug/ann-topomaps

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Visualizing Deep Neural Networks with Topographic Activation Maps

https://github.com/valeriobelcamino/unipa_inner_speech

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A Social Robot with Inner Speech for Dietary Guidance

https://github.com/vanbanTruong/FAHT

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FAHT: An Adaptive Fairness-aware Decision Tree Classifier

https://github.com/vanderschaarlab/invconban

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Inverse Contextual Bandits: Learning How Behavior Evolves over Time

https://github.com/vanderschaarlab/mlforhealthlabpub

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Scalable Bayesian Inverse Reinforcement Learning

https://github.com/vanderschaarlab/mlforhealthlabpub/tree/main/alg/edm

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Strictly Batch Imitation Learning by Energy-based Distribution Matching

https://github.com/vanderschaarlab/mlforhealthlabpub/tree/main/alg/interpole

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Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning

https://github.com/vanderschaarlab/mlforhealthlabpub/tree/main/alg/rnn-blockwise-jackknife

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Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions

https://github.com/vaseline555/aaggff

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Pursuing Overall Welfare in Federated Learning through Sequential Decision Making

https://github.com/vcl-iisc/robust-few-shot-learning

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Robust Few-shot Learning Without Using any Adversarial Samples

https://github.com/vdblm/CausalPFN

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CausalPFN: Amortized Causal Effect Estimation via In-Context Learning

https://github.com/vdblm/ExPerior

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Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity

https://github.com/vectorinstitute/neuralkernelbandits

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Empirical analysis of representation learning and exploration in neural kernel bandits

https://github.com/veda-s4dhak/AQIL

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Augmented Q Imitation Learning (AQIL)

https://github.com/vekteur/probabilistic-calibration-study

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A Large-Scale Study of Probabilistic Calibration in Neural Network Regression

https://github.com/vekteur/quantile-recalibration-training

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Probabilistic Calibration by Design for Neural Network Regression

https://github.com/veri-q/fairness

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Verifying Fairness in Quantum Machine Learning

https://github.com/veronicium/doclens

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DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation

https://github.com/vicariousgreg/neuroceril

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NeuroCERIL: Robotic Imitation Learning via Hierarchical Cause-Effect Reasoning in Programmable Attractor Neural Networks

https://github.com/victordarvariu/solving-graph-pgg

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Solving Graph-based Public Good Games with Tree Search and Imitation Learning

https://github.com/vijaykamble/vector-repeated-games

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An Approximate Dynamic Programming Approach to Adversarial Online Learning

https://github.com/vijaykeswani/closed-loop-task-allocation

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Designing Closed-Loop Models for Task Allocation

https://github.com/vijeth27/learningwithhist

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Online Decision Making with History-Average Dependent Costs (Extended)

https://github.com/vikranth3140/movie-revenue-prediction

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Movie Revenue Prediction using Machine Learning Models

https://github.com/vimalabs/VIMABench

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VIMA: General Robot Manipulation with Multimodal Prompts

https://github.com/vincent-1125/uncertainty-quantification-on-clinical-trial-outcome-prediction

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Uncertainty Quantification on Clinical Trial Outcome Prediction

https://github.com/vineet1992/Gene-Selection

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A Pipeline for Integrated Theory and Data-Driven Modeling of Genomic and Clinical Data

https://github.com/vineetjain96/merlin

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Learning to Reach Goals via Diffusion

https://github.com/viniciusguigo/kairos_minerl_basalt

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Combining Learning from Human Feedback and Knowledge Engineering to Solve Hierarchical Tasks in Minecraft

https://github.com/vios-s/mri_sequential_active_sampling

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Active Sampling for MRI-based Sequential Decision Making

https://github.com/vis-nlp/chartgemma

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ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild

https://github.com/vis-sustech/visual-analytics-for-emo-algorithm-comparison

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A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimization

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