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Found 6 papers, 2 papers with code
Disentanglement Learning via Topology
We propose TopDis (Topological Disentanglement), a method for learning disentangled representations via adding multi-scale topological loss term.
Learning Topology-Preserving Data Representations
The method aims to provide topological similarity between the data manifold and its latent representation via enforcing the similarity in topological features (clusters, loops, 2D voids, etc.)
Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting
Global warming made the Arctic available for marine operations and created demand for reliable operational sea ice forecasts to make them safe.
Transfer learning for ensembles: reducing computation time and keeping the diversity
Our approach for the transfer learning of ensembles consists of two steps: (a) shifting weights of encoders of all models in the ensemble by a single shift vector and (b) doing a tiny fine-tuning for each individual model afterwards.
Representation Topology Divergence: A Method for Comparing Neural Network Representations
Comparison of data representations is a complex multi-aspect problem that has not enjoyed a complete solution yet.
Representation Topology Divergence: A Method for Comparing Neural Network Representations.
We propose a method for comparing two data representations.
