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Found 9 papers, 6 papers with code
Neural-based classification rule learning for sequential data
Discovering interpretable patterns for classification of sequential data is of key importance for a variety of fields, ranging from genomics to fraud detection or more generally interpretable decision-making.
Differentiable Rule Induction with Learned Relational Features
On benchmark tasks, we show that these learned literals are simple enough to retain interpretability, yet improve prediction accuracy and provide sets of rules that are more concise compared to state-of-the-art rule induction algorithms.
Discovering PDEs from Multiple Experiments
Automated model discovery of partial differential equations (PDEs) usually considers a single experiment or dataset to infer the underlying governing equations.
Sparsistent Model Discovery
Discovering the partial differential equations underlying spatio-temporal datasets from very limited and highly noisy observations is of paramount interest in many scientific fields.
Fully differentiable model discovery
Model discovery aims at autonomously discovering differential equations underlying a dataset.
Model discovery in the sparse sampling regime
To improve the physical understanding and the predictions of complex dynamic systems, such as ocean dynamics and weather predictions, it is of paramount interest to identify interpretable models from coarsely and off-grid sampled observations.
Sparsely constrained neural networks for model discovery of PDEs
Sparse regression on a library of candidate features has developed as the prime method to discover the partial differential equation underlying a spatio-temporal data-set.
Temporal Normalizing Flows
Analyzing and interpreting time-dependent stochastic data requires accurate and robust density estimation.
DeepMoD: Deep learning for Model Discovery in noisy data
We introduce DeepMoD, a Deep learning based Model Discovery algorithm.
