22,914 research outputs found
Low Size-Complexity Inductive Logic Programming: The East-West Challenge Considered as a Problem in Cost-Sensitive Classification
The Inductive Logic Programming community has considered
proof-complexity and model-complexity, but, until recently,
size-complexity has received little attention. Recently a
challenge was issued "to the international computing community"
to discover low size-complexity Prolog programs for classifying
trains. The challenge was based on a problem first proposed by
Ryszard Michalski, 20 years ago. We interpreted the challenge
as a problem in cost-sensitive classification and we applied a
recently developed cost-sensitive classifier to the competition.
Our algorithm was relatively successful (we won a prize). This
paper presents our algorithm and analyzes the results of the
competition
Learning Less is More - 6D Camera Localization via 3D Surface Regression
Popular research areas like autonomous driving and augmented reality have
renewed the interest in image-based camera localization. In this work, we
address the task of predicting the 6D camera pose from a single RGB image in a
given 3D environment. With the advent of neural networks, previous works have
either learned the entire camera localization process, or multiple components
of a camera localization pipeline. Our key contribution is to demonstrate and
explain that learning a single component of this pipeline is sufficient. This
component is a fully convolutional neural network for densely regressing
so-called scene coordinates, defining the correspondence between the input
image and the 3D scene space. The neural network is prepended to a new
end-to-end trainable pipeline. Our system is efficient, highly accurate, robust
in training, and exhibits outstanding generalization capabilities. It exceeds
state-of-the-art consistently on indoor and outdoor datasets. Interestingly,
our approach surpasses existing techniques even without utilizing a 3D model of
the scene during training, since the network is able to discover 3D scene
geometry automatically, solely from single-view constraints.Comment: CVPR 201
The Algorithmic Origins of Life
Although it has been notoriously difficult to pin down precisely what it is
that makes life so distinctive and remarkable, there is general agreement that
its informational aspect is one key property, perhaps the key property. The
unique informational narrative of living systems suggests that life may be
characterized by context-dependent causal influences, and in particular, that
top-down (or downward) causation -- where higher-levels influence and constrain
the dynamics of lower-levels in organizational hierarchies -- may be a major
contributor to the hierarchal structure of living systems. Here we propose that
the origin of life may correspond to a physical transition associated with a
shift in causal structure, where information gains direct, and
context-dependent causal efficacy over the matter it is instantiated in. Such a
transition may be akin to more traditional physical transitions (e.g.
thermodynamic phase transitions), with the crucial distinction that determining
which phase (non-life or life) a given system is in requires dynamical
information and therefore can only be inferred by identifying causal
architecture. We discuss some potential novel research directions based on this
hypothesis, including potential measures of such a transition that may be
amenable to laboratory study, and how the proposed mechanism corresponds to the
onset of the unique mode of (algorithmic) information processing characteristic
of living systems.Comment: 13 pages, 1 tabl
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