232,027 research outputs found
A tutorial on recursive models for analyzing and predicting path choice behavior
The problem at the heart of this tutorial consists in modeling the path
choice behavior of network users. This problem has been extensively studied in
transportation science, where it is known as the route choice problem. In this
literature, individuals' choice of paths are typically predicted using discrete
choice models. This article is a tutorial on a specific category of discrete
choice models called recursive, and it makes three main contributions: First,
for the purpose of assisting future research on route choice, we provide a
comprehensive background on the problem, linking it to different fields
including inverse optimization and inverse reinforcement learning. Second, we
formally introduce the problem and the recursive modeling idea along with an
overview of existing models, their properties and applications. Third, we
extensively analyze illustrative examples from different angles so that a
novice reader can gain intuition on the problem and the advantages provided by
recursive models in comparison to path-based ones
Enabling Robots to Communicate their Objectives
The overarching goal of this work is to efficiently enable end-users to
correctly anticipate a robot's behavior in novel situations. Since a robot's
behavior is often a direct result of its underlying objective function, our
insight is that end-users need to have an accurate mental model of this
objective function in order to understand and predict what the robot will do.
While people naturally develop such a mental model over time through observing
the robot act, this familiarization process may be lengthy. Our approach
reduces this time by having the robot model how people infer objectives from
observed behavior, and then it selects those behaviors that are maximally
informative. The problem of computing a posterior over objectives from observed
behavior is known as Inverse Reinforcement Learning (IRL), and has been applied
to robots learning human objectives. We consider the problem where the roles of
human and robot are swapped. Our main contribution is to recognize that unlike
robots, humans will not be exact in their IRL inference. We thus introduce two
factors to define candidate approximate-inference models for human learning in
this setting, and analyze them in a user study in the autonomous driving
domain. We show that certain approximate-inference models lead to the robot
generating example behaviors that better enable users to anticipate what it
will do in novel situations. Our results also suggest, however, that additional
research is needed in modeling how humans extrapolate from examples of robot
behavior.Comment: RSS 201
PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization
Learning neural networks using only few available information is an important
ongoing research topic with tremendous potential for applications. In this
paper, we introduce a powerful regularizer for the variational modeling of
inverse problems in imaging. Our regularizer, called patch normalizing flow
regularizer (patchNR), involves a normalizing flow learned on small patches of
very few images. In particular, the training is independent of the considered
inverse problem such that the same regularizer can be applied for different
forward operators acting on the same class of images. By investigating the
distribution of patches versus those of the whole image class, we prove that
our model is indeed a MAP approach. Numerical examples for low-dose and
limited-angle computed tomography (CT) as well as superresolution of material
images demonstrate that our method provides very high quality results. The
training set consists of just six images for CT and one image for
superresolution. Finally, we combine our patchNR with ideas from internal
learning for performing superresolution of natural images directly from the
low-resolution observation without knowledge of any high-resolution image
Location-aware computing: a neural network model for determining location in wireless LANs
The strengths of the RF signals arriving from more access points in a wireless LANs are related to the position of the mobile terminal and can be used to derive the location of the user. In a heterogeneous environment, e.g. inside a building or in a variegated urban geometry, the received power is a very complex function of the distance, the geometry, the materials. The complexity of the inverse problem (to derive the position from the signals) and the lack of complete information, motivate to consider flexible models based on a network of functions (neural networks). Specifying the value of the free parameters of the model requires a supervised learning strategy that starts from a set of labeled examples to construct a model that will then generalize in an appropriate manner when confronted with new data, not present in the training set. The advantage of the method is that it does not require ad-hoc infrastructure in addition to the wireless LAN, while the flexible modeling and learning capabilities of neural networks achieve lower errors in determining the position, are amenable to incremental improvements, and do not require the detailed knowledge of the access point locations and of the building characteristics. A user needs only a map of the working space and a small number of identified locations to train a system, as evidenced by the experimental results presented
Anti-Aliasing Add-On for Deep Prior Seismic Data Interpolation
Data interpolation is a fundamental step in any seismic processing workflow.
Among machine learning techniques recently proposed to solve data interpolation
as an inverse problem, Deep Prior paradigm aims at employing a convolutional
neural network to capture priors on the data in order to regularize the
inversion. However, this technique lacks of reconstruction precision when
interpolating highly decimated data due to the presence of aliasing. In this
work, we propose to improve Deep Prior inversion by adding a directional
Laplacian as regularization term to the problem. This regularizer drives the
optimization towards solutions that honor the slopes estimated from the
interpolated data low frequencies. We provide some numerical examples to
showcase the methodology devised in this manuscript, showing that our results
are less prone to aliasing also in presence of noisy and corrupted data
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