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Found 5 papers, 3 papers with code
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers
We introduce ODEFormer, the first transformer able to infer multidimensional ordinary differential equation (ODE) systems in symbolic form from the observation of a single solution trajectory.
Predicting Ordinary Differential Equations with Transformers
We develop a transformer-based sequence-to-sequence model that recovers scalar ordinary differential equations (ODEs) in symbolic form from irregularly sampled and noisy observations of a single solution trajectory.
Discovering ordinary differential equations that govern time-series
Natural laws are often described through differential equations yet finding a differential equation that describes the governing law underlying observed data is a challenging and still mostly manual task.
Curiously Effective Features for Image Quality Prediction
The performance of visual quality prediction models is commonly assumed to be closely tied to their ability to capture perceptually relevant image aspects.
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark
Explainable Artificial Intelligence (XAI) is targeted at understanding how models perform feature selection and derive their classification decisions.
