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Latent Representation Prediction Networks
The prediction function is used as a forward model for search on a graph in a viewpoint-matching task and the representation learned to maximize predictability is found to outperform a pre-trained representation.
Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening
We propose Power Slow Feature Analysis, a gradient-based method to extract temporally slow features from a high-dimensional input stream that varies on a faster time-scale, as a variant of Slow Feature Analysis (SFA) that allows end-to-end training of arbitrary differentiable architectures and thereby significantly extends the class of models that can effectively be used for slow feature extraction.
Dual SVM Training on a Budget
We present a dual subspace ascent algorithm for support vector machine training that respects a budget constraint limiting the number of support vectors.
