Spline Filters For End-to-End Deep LearningRandall BalestrieroRomain CosentinoHervé GlotinRichard G. BaraniukPMLRInternational Conference on Machine Learning
While deep hierarchical representations dramatically reduce the number filters required for solving many tasks, still an enormous number of activation function values (activations) need to be stored in memory for learning these filters, especially when images are processed in parallel in (mini-)batches...
The tools used in developing these approaches are also quite varied and include Kalman filters [15], hidden Markov models [22], Gaussian processes [20], Bayesian networks [14], Gaussian mixture models [9], and neural networks [6]. These studies all formulate the problem of trajectory ...
The tools used in developing these approaches are also quite varied and include Kalman filters [15], hidden Markov models [22], Gaussian processes [20], Bayesian networks [14], Gaussian mixture models [9], and neural networks [6]. These studies all formulate the problem of trajectory ...