https://github.com/ALRhub/action-conditional-rkn
공식 코드Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/BME-SmartLab/GraphConvWat
공식 코드Reconstructing nodal pressures in water distribution systems with graph neural networks
https://github.com/DentonJC/DIT
공식 코드https://github.com/FlyN-Nick/MemorAIs_Frontend
공식 코드https://github.com/ModelZoos/ModelZooDataset
공식 코드Model Zoos: A Dataset of Diverse Populations of Neural Network Models
https://github.com/Physics-aware-AI/DiffCoSim
공식 코드Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models
https://github.com/arc-l/dipn
공식 코드DIPN: Deep Interaction Prediction Network with Application to Clutter Removal
https://github.com/baggepinnen/LPVSpectral.jl
공식 코드Machine Learning and System Identification for Estimation in Physical Systems
https://github.com/baggepinnen/LTVModels.jl
공식 코드https://github.com/baggepinnen/Robotlib.jl
공식 코드Machine Learning and System Identification for Estimation in Physical Systems
https://github.com/biaslab/IWAI2020-onlinesysid
공식 코드Online system identification in a Duffing oscillator by free energy minimisation
https://github.com/clearoboticslab/tsymrl
공식 코드An Investigation of Time Reversal Symmetry in Reinforcement Learning
https://github.com/csu-signal/faaf_acl
공식 코드Frictional Agent Alignment Framework: Slow Down and Don't Break Things
https://github.com/davidcotton/gym-d2d
공식 코드GymD2D: A Device-to-Device Underlay Cellular Offload Evaluation Platform
https://github.com/dioscuri-centre/biofilms_on_corrugated_surfaces
공식 코드Substrate geometry affects population dynamics in a bacterial biofilm
https://github.com/dyco-uparis/EcoDyco
공식 코드Macroeconomic Dynamics in a finite world: the Thermodynamic Potential Approach
https://github.com/evernorif/pugs
공식 코드PUGS: Zero-shot Physical Understanding with Gaussian Splatting
https://github.com/f-haces/lidarmanning
공식 코드https://github.com/facebookresearch/emg2qwerty
공식 코드emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography
https://github.com/for-ai/portability
공식 코드The Grand Illusion: The Myth of Software Portability and Implications for ML Progress
https://github.com/gabrieleenx/frictionmodelling
공식 코드Planar Friction Modelling with LuGre Dynamics and Limit Surfaces
https://github.com/guisf/rgd
공식 코드https://github.com/huiyu-2002/ifactformer-m
공식 코드Implicit factorized transformer approach to fast prediction of turbulent channel flows
https://github.com/karlji1021/cheetah-software
공식 코드https://github.com/l-marino/switching-gplfm
공식 코드https://github.com/maitreyapatel/cripp-vqa
공식 코드CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question Answering
https://github.com/matthewpurri/Teaching-Cameras-to-Feel
공식 코드Teaching Cameras to Feel: Estimating Tactile Physical Properties of Surfaces From Images
https://github.com/michaelmarav/lcd
공식 코드https://github.com/microsoft/microxcaling
공식 코드https://github.com/mlcommons/croissant
공식 코드https://github.com/msahamed/earthquake_physics_bayesian_nn
공식 코드Estimating uncertainty of earthquake rupture using Bayesian neural network
https://github.com/msahamed/machine_learning_earthquake_rupture
공식 코드https://github.com/nikolaiwest/pyscrew
공식 코드PyScrew: A Comprehensive Dataset Collection from Industrial Screw Driving Experiments
https://github.com/paoloae/paper_rl_blackbox_thermal_machines
공식 코드https://github.com/petrikvladimir/video_skills_learning_with_approx_physics
공식 코드Learning Object Manipulation Skills from Video via Approximate Differentiable Physics
https://github.com/petrikvladimir/video_skills_learning_with_aprox_physics
공식 코드Learning Object Manipulation Skills from Video via Approximate Differentiable Physics
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.