6,031 research outputs found
Task-Driven Dictionary Learning
Modeling data with linear combinations of a few elements from a learned
dictionary has been the focus of much recent research in machine learning,
neuroscience and signal processing. For signals such as natural images that
admit such sparse representations, it is now well established that these models
are well suited to restoration tasks. In this context, learning the dictionary
amounts to solving a large-scale matrix factorization problem, which can be
done efficiently with classical optimization tools. The same approach has also
been used for learning features from data for other purposes, e.g., image
classification, but tuning the dictionary in a supervised way for these tasks
has proven to be more difficult. In this paper, we present a general
formulation for supervised dictionary learning adapted to a wide variety of
tasks, and present an efficient algorithm for solving the corresponding
optimization problem. Experiments on handwritten digit classification, digital
art identification, nonlinear inverse image problems, and compressed sensing
demonstrate that our approach is effective in large-scale settings, and is well
suited to supervised and semi-supervised classification, as well as regression
tasks for data that admit sparse representations.Comment: final draft post-refereein
Task-Driven Estimation and Control via Information Bottlenecks
Our goal is to develop a principled and general algorithmic framework for
task-driven estimation and control for robotic systems. State-of-the-art
approaches for controlling robotic systems typically rely heavily on accurately
estimating the full state of the robot (e.g., a running robot might estimate
joint angles and velocities, torso state, and position relative to a goal).
However, full state representations are often excessively rich for the specific
task at hand and can lead to significant computational inefficiency and
brittleness to errors in state estimation. In contrast, we present an approach
that eschews such rich representations and seeks to create task-driven
representations. The key technical insight is to leverage the theory of
information bottlenecks}to formalize the notion of a "task-driven
representation" in terms of information theoretic quantities that measure the
minimality of a representation. We propose novel iterative algorithms for
automatically synthesizing (offline) a task-driven representation (given in
terms of a set of task-relevant variables (TRVs)) and a performant control
policy that is a function of the TRVs. We present online algorithms for
estimating the TRVs in order to apply the control policy. We demonstrate that
our approach results in significant robustness to unmodeled measurement
uncertainty both theoretically and via thorough simulation experiments
including a spring-loaded inverted pendulum running to a goal location.Comment: 9 pages, 4 figures, abridged version accepted to ICRA2019;
Incorporates changes in final conference submissio
Task-driven tools for requirements engineering
This research aims at designing and evaluating a
new generation of usable and multimodal Require ments and Analysis Tools, capable of promoting arti fact co-evolution in a useful manner, enabling coop eration and communication of multiple stakeholders
over a common semantic model. The main goal is to
leverage the elicitation of functional and non functional requirements by using multimodal interac tion techniques, and driving software development
using a conceptual architecture easily extracted from
user task flows.info:eu-repo/semantics/publishedVersio
Task-Driven Adaptive Statistical Compressive Sensing of Gaussian Mixture Models
A framework for adaptive and non-adaptive statistical compressive sensing is
developed, where a statistical model replaces the standard sparsity model of
classical compressive sensing. We propose within this framework optimal
task-specific sensing protocols specifically and jointly designed for
classification and reconstruction. A two-step adaptive sensing paradigm is
developed, where online sensing is applied to detect the signal class in the
first step, followed by a reconstruction step adapted to the detected class and
the observed samples. The approach is based on information theory, here
tailored for Gaussian mixture models (GMMs), where an information-theoretic
objective relationship between the sensed signals and a representation of the
specific task of interest is maximized. Experimental results using synthetic
signals, Landsat satellite attributes, and natural images of different sizes
and with different noise levels show the improvements achieved using the
proposed framework when compared to more standard sensing protocols. The
underlying formulation can be applied beyond GMMs, at the price of higher
mathematical and computational complexity
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