1,834 research outputs found
Characterizing the Shape of Activation Space in Deep Neural Networks
The representations learned by deep neural networks are difficult to
interpret in part due to their large parameter space and the complexities
introduced by their multi-layer structure. We introduce a method for computing
persistent homology over the graphical activation structure of neural networks,
which provides access to the task-relevant substructures activated throughout
the network for a given input. This topological perspective provides unique
insights into the distributed representations encoded by neural networks in
terms of the shape of their activation structures. We demonstrate the value of
this approach by showing an alternative explanation for the existence of
adversarial examples. By studying the topology of network activations across
multiple architectures and datasets, we find that adversarial perturbations do
not add activations that target the semantic structure of the adversarial class
as previously hypothesized. Rather, adversarial examples are explainable as
alterations to the dominant activation structures induced by the original
image, suggesting the class representations learned by deep networks are
problematically sparse on the input space
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
Learning Finer-class Networks for Universal Representations
Many real-world visual recognition use-cases can not directly benefit from
state-of-the-art CNN-based approaches because of the lack of many annotated
data. The usual approach to deal with this is to transfer a representation
pre-learned on a large annotated source-task onto a target-task of interest.
This raises the question of how well the original representation is
"universal", that is to say directly adapted to many different target-tasks. To
improve such universality, the state-of-the-art consists in training networks
on a diversified source problem, that is modified either by adding generic or
specific categories to the initial set of categories. In this vein, we proposed
a method that exploits finer-classes than the most specific ones existing, for
which no annotation is available. We rely on unsupervised learning and a
bottom-up split and merge strategy. We show that our method learns more
universal representations than state-of-the-art, leading to significantly
better results on 10 target-tasks from multiple domains, using several network
architectures, either alone or combined with networks learned at a coarser
semantic level.Comment: British Machine Vision Conference (BMVC) 201
GOGGLES: Automatic Image Labeling with Affinity Coding
Generating large labeled training data is becoming the biggest bottleneck in
building and deploying supervised machine learning models. Recently, the data
programming paradigm has been proposed to reduce the human cost in labeling
training data. However, data programming relies on designing labeling functions
which still requires significant domain expertise. Also, it is prohibitively
difficult to write labeling functions for image datasets as it is hard to
express domain knowledge using raw features for images (pixels).
We propose affinity coding, a new domain-agnostic paradigm for automated
training data labeling. The core premise of affinity coding is that the
affinity scores of instance pairs belonging to the same class on average should
be higher than those of pairs belonging to different classes, according to some
affinity functions. We build the GOGGLES system that implements affinity coding
for labeling image datasets by designing a novel set of reusable affinity
functions for images, and propose a novel hierarchical generative model for
class inference using a small development set.
We compare GOGGLES with existing data programming systems on 5 image labeling
tasks from diverse domains. GOGGLES achieves labeling accuracies ranging from a
minimum of 71% to a maximum of 98% without requiring any extensive human
annotation. In terms of end-to-end performance, GOGGLES outperforms the
state-of-the-art data programming system Snuba by 21% and a state-of-the-art
few-shot learning technique by 5%, and is only 7% away from the fully
supervised upper bound.Comment: Published at 2020 ACM SIGMOD International Conference on Management
of Dat
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