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Found 5 papers, 2 papers with code
SlotGNN: Unsupervised Discovery of Multi-Object Representations and Visual Dynamics
Our SlotGNN, a novel unsupervised graph-based dynamics model, predicts the future state of multi-object scenes.
Hierarchical Graph Neural Networks for Proprioceptive 6D Pose Estimation of In-hand Objects
We evaluate our model on a diverse subset of objects from the YCB Object and Model Set, and show that our method substantially outperforms existing state-of-the-art work in accuracy and robustness to occlusion.
KINet: Unsupervised Forward Models for Robotic Pushing Manipulation
Using visual observations, our model learns to associate objects with keypoint coordinates and discovers a graph representation of the system as a set of keypoint embeddings and their relations.
Explainable Ensemble Machine Learning for Breast Cancer Diagnosis based on Ultrasound Image Texture Features
Each decision tree learns to classify the input ultrasound image by learning a set of robust decision thresholds for texture features of the image.
A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine Learning Approach
Predicting the outcome of sales opportunities is a core part of successful business management.
