Abstract:
Many supervised learning problems involve high-dimensional data such as images, text, or graphs. In order to make efficient use of data, it is often useful to leverage priors in the problem at hand, such as invariance to certain transformations or stability to small deformations. Empirically, deep convolutional networks have been very successful on such data, raising the question of...