4 research outputs found
Within-layer Diversity Reduces Generalization Gap
Neural networks are composed of multiple layers arranged in a hierarchical
structure jointly trained with a gradient-based optimization, where the errors
are back-propagated from the last layer back to the first one. At each
optimization step, neurons at a given layer receive feedback from neurons
belonging to higher layers of the hierarchy. In this paper, we propose to
complement this traditional 'between-layer' feedback with additional
'within-layer' feedback to encourage diversity of the activations within the
same layer. To this end, we measure the pairwise similarity between the outputs
of the neurons and use it to model the layer's overall diversity. By penalizing
similarities and promoting diversity, we encourage each neuron to learn a
distinctive representation and, thus, to enrich the data representation learned
within the layer and to increase the total capacity of the model. We
theoretically study how the within-layer activation diversity affects the
generalization performance of a neural network and prove that increasing the
diversity of hidden activations reduces the estimation error. In addition to
the theoretical guarantees, we present an empirical study on three datasets
confirming that the proposed approach enhances the performance of
state-of-the-art neural network models and decreases the generalization gap.Comment: 18 pages, 1 figure, 3 Table
Multi-layer ratio semi-definite classifiers
We develop a novel extension to the Ratio Semi-definite Classifier, a discriminative model formulated as a ratio of semi-definite polynomials. By adding a hidden layer to the model, we can efficiently train the model, while achieving higher accuracy than the original version. Results on artificial 2-D data as well as two separate phone classification corpora show that our multi-layer model still avoids the overconfidence bias found in models based on ratios of exponentials, while remaining competitive with state-of-the-art techniques such as multi-layer perceptrons. Index Terms β Pattern recognition, Speech recognition 1