21,029 research outputs found
Tensor Regression Networks
Convolutional neural networks typically consist of many convolutional layers
followed by one or more fully connected layers. While convolutional layers map
between high-order activation tensors, the fully connected layers operate on
flattened activation vectors. Despite empirical success, this approach has
notable drawbacks. Flattening followed by fully connected layers discards
multilinear structure in the activations and requires many parameters. We
address these problems by incorporating tensor algebraic operations that
preserve multilinear structure at every layer. First, we introduce Tensor
Contraction Layers (TCLs) that reduce the dimensionality of their input while
preserving their multilinear structure using tensor contraction. Next, we
introduce Tensor Regression Layers (TRLs), which express outputs through a
low-rank multilinear mapping from a high-order activation tensor to an output
tensor of arbitrary order. We learn the contraction and regression factors
end-to-end, and produce accurate nets with fewer parameters. Additionally, our
layers regularize networks by imposing low-rank constraints on the activations
(TCL) and regression weights (TRL). Experiments on ImageNet show that, applied
to VGG and ResNet architectures, TCLs and TRLs reduce the number of parameters
compared to fully connected layers by more than 65% while maintaining or
increasing accuracy. In addition to the space savings, our approach's ability
to leverage topological structure can be crucial for structured data such as
MRI. In particular, we demonstrate significant performance improvements over
comparable architectures on three tasks associated with the UK Biobank dataset
On Network Science and Mutual Information for Explaining Deep Neural Networks
In this paper, we present a new approach to interpret deep learning models.
By coupling mutual information with network science, we explore how information
flows through feedforward networks. We show that efficiently approximating
mutual information allows us to create an information measure that quantifies
how much information flows between any two neurons of a deep learning model. To
that end, we propose NIF, Neural Information Flow, a technique for codifying
information flow that exposes deep learning model internals and provides
feature attributions.Comment: ICASSP 2020 (shorter version appeared at AAAI-19 Workshop on Network
Interpretability for Deep Learning
Visualizing and Understanding Sum-Product Networks
Sum-Product Networks (SPNs) are recently introduced deep tractable
probabilistic models by which several kinds of inference queries can be
answered exactly and in a tractable time. Up to now, they have been largely
used as black box density estimators, assessed only by comparing their
likelihood scores only. In this paper we explore and exploit the inner
representations learned by SPNs. We do this with a threefold aim: first we want
to get a better understanding of the inner workings of SPNs; secondly, we seek
additional ways to evaluate one SPN model and compare it against other
probabilistic models, providing diagnostic tools to practitioners; lastly, we
want to empirically evaluate how good and meaningful the extracted
representations are, as in a classic Representation Learning framework. In
order to do so we revise their interpretation as deep neural networks and we
propose to exploit several visualization techniques on their node activations
and network outputs under different types of inference queries. To investigate
these models as feature extractors, we plug some SPNs, learned in a greedy
unsupervised fashion on image datasets, in supervised classification learning
tasks. We extract several embedding types from node activations by filtering
nodes by their type, by their associated feature abstraction level and by their
scope. In a thorough empirical comparison we prove them to be competitive
against those generated from popular feature extractors as Restricted Boltzmann
Machines. Finally, we investigate embeddings generated from random
probabilistic marginal queries as means to compare other tractable
probabilistic models on a common ground, extending our experiments to Mixtures
of Trees.Comment: Machine Learning Journal paper (First Online), 24 page
Deep Fishing: Gradient Features from Deep Nets
Convolutional Networks (ConvNets) have recently improved image recognition
performance thanks to end-to-end learning of deep feed-forward models from raw
pixels. Deep learning is a marked departure from the previous state of the art,
the Fisher Vector (FV), which relied on gradient-based encoding of local
hand-crafted features. In this paper, we discuss a novel connection between
these two approaches. First, we show that one can derive gradient
representations from ConvNets in a similar fashion to the FV. Second, we show
that this gradient representation actually corresponds to a structured matrix
that allows for efficient similarity computation. We experimentally study the
benefits of transferring this representation over the outputs of ConvNet
layers, and find consistent improvements on the Pascal VOC 2007 and 2012
datasets.Comment: To appear at BMVC 201
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