https://github.com/humancompatibleai/eirli
공식 코드An Empirical Investigation of Representation Learning for Imitation
Papers with Code 아카이브
Decision Making
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/humancompatibleai/il-representations
공식 코드An Empirical Investigation of Representation Learning for Imitation
https://github.com/humblef-oo-l/DP
공식 코드Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry
https://github.com/hungntt/xai_thyroid
공식 코드https://github.com/huntertracer/glinsat
공식 코드GLinSAT: The General Linear Satisfiability Neural Network Layer By Accelerated Gradient Descent
https://github.com/huoyongtony/rebound-winner-takes-all
공식 코드https://github.com/huytu7/frugal
공식 코드https://github.com/hvarfner/jointentropysearch
공식 코드Joint Entropy Search for Maximally-Informed Bayesian Optimization
https://github.com/hwansoo-choi/cognitive-transfuser
공식 코드https://github.com/hwdance/ssvgp
공식 코드Spectral Representations for Accurate Causal Uncertainty Quantification with Gaussian Processes
https://github.com/hwiberg/opticl
공식 코드https://github.com/hwj20/apex_exp
공식 코드APEX: Empowering LLMs with Physics-Based Task Planning for Real-time Insight
https://github.com/hxixixh/adaflow
공식 코드AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies
https://github.com/hxlyn3/diaster
공식 코드Episodic Return Decomposition by Difference of Implicitly Assigned Sub-Trajectory Reward
https://github.com/hyesunyun/medsysreviewsfromllms
공식 코드Appraising the Potential Uses and Harms of LLMs for Medical Systematic Reviews
https://github.com/i-to-the-power-i/causal-lz-p-decision-tree
공식 코드Causal Discovery and Classification Using Lempel-Ziv Complexity
https://github.com/i22024/lvlm2p
공식 코드Sample Efficient Reinforcement Learning via Large Vision Language Model Distillation
https://github.com/i6092467/diff-bias-proxies
공식 코드Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods
https://github.com/iag0g0mes/t2fis_driving_style
공식 코드https://github.com/iain-burge/quantumshapleyvaluealgorithm
공식 코드A Shapley Value Estimation Speedup for Efficient Explainable Quantum AI
https://github.com/iamhankai/Forest-of-Thought
공식 코드Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning
https://github.com/iamtheo2000/musiclime
공식 코드https://github.com/ian-jihoonpark/X-Diffusion
공식 코드Explaining generative diffusion models via visual analysis for interpretable decision-making process
https://github.com/ianRDavies/LeMOL
공식 코드https://github.com/iasawseen/MultiServerRL
공식 코드Artificial Intelligence for Prosthetics - challenge solutions
https://github.com/ibm-hrl-mlhls/bicause-trees
공식 코드Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
https://github.com/ibm/swatgym
공식 코드A SWAT-based Reinforcement Learning Framework for Crop Management
https://github.com/icitylab/traffnet
공식 코드TraffNet: Learning Causality of Traffic Generation for What-if Prediction
https://github.com/ictnlp/auto-rag
공식 코드Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Models
https://github.com/id9502/Option-GAIL
공식 코드https://github.com/ideas-labo/aspiration-study
공식 코드Do Performance Aspirations Matter for Guiding Software Configuration Tuning?
https://github.com/ideechy/Online-Decision-Making
공식 코드Statistical Inference for Online Decision Making via Stochastic Gradient Descent
https://github.com/idejie/KAD
공식 코드Active Object Detection with Knowledge Aggregation and Distillation from Large Models
https://github.com/iiis-ai/cumulative-reasoning
공식 코드https://github.com/iipl-hangzhoudianziuniversity/defn-pytorch
공식 코드https://github.com/ilevyfan/meta_rl_scaling
공식 코드A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.