[Lisa_teatalk] Tea talk this Week Friday 3PM in LISA room

Razvan Pascanu r.pascanu at gmail.com
Wed Nov 13 09:18:40 EST 2013


Hi all,

 This week we will have a tea talk where Caglar, Kyunghyun and myself will
present the work submitted at AISTATS : Lp units for MLPs.

The talk will try to go beyond what with submitted. We will try to
summarize some ideas we have to extend this work, namely my allowing more
flexibility in the units. I will also talk about how one can visualize what
these units are doing and how that could be useful.

The abstract of the paper:

In this paper we proposed a novel nonlinear unit, which is called as Lp
unit, for a multi-layer perceptron (MLP). The proposed Lp unit receives
signal from several projections of the layer below and computes the
normalized Lp norm. We notice two interesting interpretations of the Lp
unit. First, we note that the proposed unit is a generalization of a number
of conventional pooling operators such as average, root-mean-square and max
pooling widely used in, for instance, convolutional neural networks(CNN),
HMAX models and neocognitrons. Furthermore, under certain constraints, the
Lp unit is a generalization of the recently proposed maxout unit
(Goodfellow et al, 2013) which achieved the state-of-the-art object
recognition results on a number of benchmark datasets. Second, we provide a
geometrical interpretation of the activation function. Each Lp unit defines
a spherical boundary, with its exact shape defined by the order p. We claim
that this makes it possible to obtain arbitrarily shaped, curved boundaries
more efficiently by combining just a few Lp units of different orders. We
empirically evaluate the proposed Lp units on a number of datasets and show
that MLPs consisting of the Lp units achieves the state-of-the-art results
on a number of benchmark datasets.


Hope to see many of you there,

Razvan
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