Papers with Code 아카이브
Logical Reasoning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 22
- 평가표
- 10
- 보존된 평가 기록
- 65
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/22strongestme/LOCO-Annotations
공식 코드LogiCode: an LLM-Driven Framework for Logical Anomaly Detection
https://github.com/3dcompat200/3dcompat200
공식 코드3DCoMPaT200: Language-Grounded Compositional Understanding of Parts and Materials of 3D Shapes
https://github.com/8188zq/AutoLogi
공식 코드https://github.com/8zym/crave
공식 코드CRAVE: A Conflicting Reasoning Approach for Explainable Claim Verification Using LLMs
https://github.com/99andBeyond/Apollo1060
공식 코드Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
https://github.com/AGMoller/worker_vs_gpt
공식 코드The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Tasks
https://github.com/AI-secure/DataLens
공식 코드DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation
https://github.com/ATR-DBI/Map-EQA
공식 코드Map-based Modular Approach for Zero-shot Embodied Question Answering
https://github.com/AjayP13/webdreamer
공식 코드https://github.com/AlexS28/SABER
공식 코드SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots
https://github.com/AmeyHengle/multilingual-long-context-reasoning
공식 코드https://github.com/Amir-pyh/LOGICOM
공식 코드https://github.com/Applied-Machine-Learning-Lab/AMAR
공식 코드https://github.com/ArashNasrEsfahani/Object-Rearrangement-in-Dynamic-Environments
공식 코드AI-Driven Relocation Tracking in Dynamic Kitchen Environments
https://github.com/CEA-LIST/AD-RobustnessEval
공식 코드Evaluating Robustness over High Level Driving Instruction for Autonomous Driving
https://github.com/CNJianLiu/Neural-Brain-for-Embodied-Agents
공식 코드Neural Brain: A Neuroscience-inspired Framework for Embodied Agents
https://github.com/D-ST-Sword/ATD
공식 코드Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method
https://github.com/Dandelionym/llm_for_mechanisms
공식 코드https://github.com/Data-Centric-GraphML/awesome-papers
공식 코드Towards Data-centric Graph Machine Learning: Review and Outlook
https://github.com/DimitrisAlivas/StarQE
공식 코드https://github.com/DongYang26/PI-DRME
공식 코드https://github.com/DreamInvoker/SIRE
공식 코드SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction
https://github.com/EdwinKim3069/XtarNet
공식 코드XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning
https://github.com/Ekim-Yurtsever/Hybrid-DeepRL-Automated-Driving
공식 코드Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving
https://github.com/Eleanor-H/DAGN
공식 코드https://github.com/EmanuelSommer/MILE
공식 코드Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks
https://github.com/FireFYF/modulatedautoencoder
공식 코드Variable Rate Deep Image Compression with Modulated Autoencoder
https://github.com/Franek18/ExKLoP
공식 코드https://github.com/GeekDream-x/IDOL
공식 코드IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning
https://github.com/Haiyang-W/MAVN
공식 코드https://github.com/HanqingWangAI/SSM-VLN
공식 코드https://github.com/Hyun-Ryu/clover
공식 코드https://github.com/IBM/AI-Hilbert
공식 코드Evolving Scientific Discovery by Unifying Data and Background Knowledge with AI Hilbert
https://github.com/IBM/AIX360
공식 코드One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
https://github.com/IBM/IBM_VizWiz
공식 코드Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.