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The present phase of Machine Learning is characterized by supervised learning algorithms relying on large sets of labeled examples (n → ∞). The next phase is likely to focus on algorithms capable of learning from very few labeled examples (n → 1), like humans seem able to do. We propose an approach to this problem and describe the underlying theory, based(More)
a r t i c l e i n f o a b s t r a c t The present phase of Machine Learning is characterized by supervised learning algorithms relying on large sets of labeled examples (n → ∞). The next phase is likely to focus on algorithms capable of learning from very few labeled examples (n → 1), like humans seem able to do. We propose an approach to this problem and(More)
We discuss data representation which can be learned automatically from data, are invariant to transformations, and at the same time selective , in the sense that two points have the same representation only if they are one the transformation of the other. The mathematical results here sharpen some of the key claims of i-theory – a recent theory of(More)
During T-cell migration, cell polarity is orchestrated by chemokine receptors and adhesion molecules and involves the functional redistribution of molecules and organelles towards specific cell compartments. In contrast, it is generally believed that the cell polarity established when T cells meet antigen-presenting cells (APCs) is controlled by the(More)
In i-theory a typical layer of a hierarchical architecture consists of HW modules pooling the dot products of the inputs to the layer with the transformations of a few templates under a group. Such layers include as special cases the convolutional layers of Deep Convolutional Networks (DCNs) as well as the non-convolutional layers (when the group contains(More)
The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and relatively robust against identity-preserving transformations like depth-rotations [33, 32, 23, 13]. Current computational models of object recognition, including recent deep learning(More)
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