In contrast, symbolic and modular models have a relatively better grounding and robustness, though at the cost of accuracy.
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the latent factors.
First, we generate images in low-frequency bands by training a sampler in the wavelet domain.
In this work, we tackle a slightly more intricate scenario where the observations are generated from a conditional distribution of some known control variate and some latent noise variate.
To solve this constrained optimization problem, our method employs Lagrange multipliers that act as integrators of error over training and identify `support vector'-like examples.
A central challenge in sensory neuroscience is describing how the activity of populations of neurons can represent useful features of the external environment.
Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization.
This paper also introduces a new ensemble construction variant that combines hyperparameter optimization with the construction of ensembles.