5,700 research outputs found
An LP-Based Approach for Goal Recognition as Planning
Goal recognition aims to recognize the set of candidate goals that are
compatible with the observed behavior of an agent. In this paper, we develop a
method based on the operator-counting framework that efficiently computes
solutions that satisfy the observations and uses the information generated to
solve goal recognition tasks. Our method reasons explicitly about both partial
and noisy observations: estimating uncertainty for the former, and satisfying
observations given the unreliability of the sensor for the latter. We evaluate
our approach empirically over a large data set, analyzing its components on how
each can impact the quality of the solutions. In general, our approach is
superior to previous methods in terms of agreement ratio, accuracy, and spread.
Finally, our approach paves the way for new research on combinatorial
optimization to solve goal recognition tasks.Comment: 8 pages, 4 tables, 3 figures. Published in AAAI 2021. Updated final
authorship and tex
Predictable Feature Analysis
Every organism in an environment, whether biological, robotic or virtual,
must be able to predict certain aspects of its environment in order to survive
or perform whatever task is intended. It needs a model that is capable of
estimating the consequences of possible actions, so that planning, control, and
decision-making become feasible. For scientific purposes, such models are
usually created in a problem specific manner using differential equations and
other techniques from control- and system-theory. In contrast to that, we aim
for an unsupervised approach that builds up the desired model in a
self-organized fashion. Inspired by Slow Feature Analysis (SFA), our approach
is to extract sub-signals from the input, that behave as predictable as
possible. These "predictable features" are highly relevant for modeling,
because predictability is a desired property of the needed
consequence-estimating model by definition. In our approach, we measure
predictability with respect to a certain prediction model. We focus here on the
solution of the arising optimization problem and present a tractable algorithm
based on algebraic methods which we call Predictable Feature Analysis (PFA). We
prove that the algorithm finds the globally optimal signal, if this signal can
be predicted with low error. To deal with cases where the optimal signal has a
significant prediction error, we provide a robust, heuristically motivated
variant of the algorithm and verify it empirically. Additionally, we give
formal criteria a prediction-model must meet to be suitable for measuring
predictability in the PFA setting and also provide a suitable default-model
along with a formal proof that it meets these criteria
Cross Pixel Optical Flow Similarity for Self-Supervised Learning
We propose a novel method for learning convolutional neural image
representations without manual supervision. We use motion cues in the form of
optical flow, to supervise representations of static images. The obvious
approach of training a network to predict flow from a single image can be
needlessly difficult due to intrinsic ambiguities in this prediction task. We
instead propose a much simpler learning goal: embed pixels such that the
similarity between their embeddings matches that between their optical flow
vectors. At test time, the learned deep network can be used without access to
video or flow information and transferred to tasks such as image
classification, detection, and segmentation. Our method, which significantly
simplifies previous attempts at using motion for self-supervision, achieves
state-of-the-art results in self-supervision using motion cues, competitive
results for self-supervision in general, and is overall state of the art in
self-supervised pretraining for semantic image segmentation, as demonstrated on
standard benchmarks
SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine
Traditional medicine typically applies one-size-fits-all treatment for the
entire patient population whereas precision medicine develops tailored
treatment schemes for different patient subgroups. The fact that some factors
may be more significant for a specific patient subgroup motivates clinicians
and medical researchers to develop new approaches to subgroup detection and
analysis, which is an effective strategy to personalize treatment. In this
study, we propose a novel patient subgroup detection method, called Supervised
Biclustring (SUBIC) using convex optimization and apply our approach to detect
patient subgroups and prioritize risk factors for hypertension (HTN) in a
vulnerable demographic subgroup (African-American). Our approach not only finds
patient subgroups with guidance of a clinically relevant target variable but
also identifies and prioritizes risk factors by pursuing sparsity of the input
variables and encouraging similarity among the input variables and between the
input and target variable
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
- …