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Found 7 papers, 2 papers with code
Balancing a 3D Inverted Pendulum using Remote Magnetic Manipulation
This greatly expands the range of potential medical applications and includes even dynamic environments as encountered in cardiovascular interventions.
What is an equivariant neural network?
We explain equivariant neural networks, a notion underlying breakthroughs in machine learning from deep convolutional neural networks for computer vision to AlphaFold 2 for protein structure prediction, without assuming knowledge of equivariance or neural networks.
Topology-Preserving Dimensionality Reduction via Interleaving Optimization
Dimensionality reduction techniques are powerful tools for data preprocessing and visualization which typically come with few guarantees concerning the topological correctness of an embedding.
Topological Regularization for Dense Prediction
Dense prediction tasks such as depth perception and semantic segmentation are important applications in computer vision that have a concrete topological description in terms of partitioning an image into connected components or estimating a function with a small number of local extrema corresponding to objects in the image.
Clustering of Electromagnetic Showers and Particle Interactions with Graph Neural Networks in Liquid Argon Time Projection Chambers Data
The optimized algorithm is then applied to the related task of clustering particle instances into interactions and yields a mean ARI of 99. 2 % for an interaction density of $\sim\mathcal{O}(1)\, m^{-3}$.
Modeling Electromagnetic Navigation Systems for Medical Applications using Random Forests and Artificial Neural Networks
In this paper, we use a random forest (RF) and an artificial neural network (ANN) to model the nonlinear behavior of the magnetic fields generated by an eMNS.
A Topology Layer for Machine Learning
Topology applied to real world data using persistent homology has started to find applications within machine learning, including deep learning.
