Logical Reasoning
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/am-bean/lingOly
공식 코드https://github.com/amayuelas/NNKGReasoning
공식 코드https://github.com/amazon-research/embert
공식 코드Embodied BERT: A Transformer Model for Embodied, Language-guided Visual Task Completion
https://github.com/amine179/curiosity-rl-for-drug-design
공식 코드Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug Design
https://github.com/amir-m-vahedi/spgd-benchmark-functions
공식 코드https://github.com/amirarcane/bertopic
공식 코드https://github.com/amirhossein-kz/awesome-diffusion-models-in-medical-imaging
공식 코드Diffusion Models for Medical Image Analysis: A Comprehensive Survey
https://github.com/amitkchakraborty/ewsofinfectiousdiseases
공식 코드An early warning indicator trained on stochastic disease-spreading models with different noises
https://github.com/andersgiovanni/worker_vs_gpt
공식 코드The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Tasks
https://github.com/andrea-martin/convergentl2o
공식 코드Learning to optimize with convergence guarantees using nonlinear system theory
https://github.com/androidarenaagent/androidarena
공식 코드Understanding the Weakness of Large Language Model Agents within a Complex Android Environment
https://github.com/aneesperingal/rul-prediction
공식 코드Remaining Useful Life Prediction for Aircraft Engines using LSTM
https://github.com/anonrabit/dialfred
공식 코드DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following
https://github.com/antoinelrnld/modlog
공식 코드MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic
https://github.com/antonxue/chordal-lipsdp
공식 코드Chordal Sparsity for Lipschitz Constant Estimation of Deep Neural Networks
https://github.com/anyloc/revisit-anything
공식 코드Revisit Anything: Visual Place Recognition via Image Segment Retrieval
https://github.com/apoorvalal/TestingInEventStudies
공식 코드When can we get away with using the two-way fixed effects regression?
https://github.com/apple/ml-interactive-data-augmentation
공식 코드https://github.com/arajv/SayNav
공식 코드SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments
https://github.com/arjunbhorkar/ReViND
공식 코드https://github.com/arsena-k/Word2Vec-bias-extraction
공식 코드Machine learning as a model for cultural learning: Teaching an algorithm what it means to be fat
https://github.com/atilman/regimeshift_wellbeing
공식 코드Maintaining human wellbeing as socio-environmental systems undergo regime shifts
https://github.com/au-clan/foa
공식 코드Fleet of Agents: Coordinated Problem Solving with Large Language Models
https://github.com/autogenai/easy-problems-that-llms-get-wrong
공식 코드https://github.com/avalanchesiqi/networked-popularity
공식 코드https://github.com/awesomericky/complex-env-navigation
공식 코드Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation
https://github.com/aymenmir1/hps
공식 코드https://github.com/azureleon1/awesome-molecular-diffusion-models
공식 코드Diffusion Models for Molecules: A Survey of Methods and Tasks
https://github.com/baha2r/Fanuc_Robotiq_Grasp
공식 코드https://github.com/bai-lab/kg-retriever
공식 코드KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models
https://github.com/banafshebamdad/InCrowd-VI
공식 코드https://github.com/bdaiinstitute/vlfm
공식 코드VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation
https://github.com/belaalb/frameGAN
공식 코드https://github.com/belindal/taskbench500
공식 코드Quantifying Adaptability in Pre-trained Language Models with 500 Tasks
https://github.com/benediktkol/recursive-noise-diffusion
공식 코드Multi-Class Segmentation from Aerial Views using Recursive Noise Diffusion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.