98 research outputs found
On the Equivalence of f-Divergence Balls and Density Bands in Robust Detection
The paper deals with minimax optimal statistical tests for two composite
hypotheses, where each hypothesis is defined by a non-parametric uncertainty
set of feasible distributions. It is shown that for every pair of uncertainty
sets of the f-divergence ball type, a pair of uncertainty sets of the density
band type can be constructed, which is equivalent in the sense that it admits
the same pair of least favorable distributions. This result implies that robust
tests under -divergence ball uncertainty, which are typically only minimax
optimal for the single sample case, are also fixed sample size minimax optimal
with respect to the equivalent density band uncertainty sets.Comment: 5 pages, 1 figure, accepted for publication in the Proceedings of the
IEEE International Conference on Acoustics, Speech, and Signal Processing
(ICASSP) 201
Efficient Constellation-Based Map-Merging for Semantic SLAM
Data association in SLAM is fundamentally challenging, and handling ambiguity
well is crucial to achieve robust operation in real-world environments. When
ambiguous measurements arise, conservatism often mandates that the measurement
is discarded or a new landmark is initialized rather than risking an incorrect
association. To address the inevitable `duplicate' landmarks that arise, we
present an efficient map-merging framework to detect duplicate constellations
of landmarks, providing a high-confidence loop-closure mechanism well-suited
for object-level SLAM. This approach uses an incrementally-computable
approximation of landmark uncertainty that only depends on local information in
the SLAM graph, avoiding expensive recovery of the full system covariance
matrix. This enables a search based on geometric consistency (GC) (rather than
full joint compatibility (JC)) that inexpensively reduces the search space to a
handful of `best' hypotheses. Furthermore, we reformulate the commonly-used
interpretation tree to allow for more efficient integration of clique-based
pairwise compatibility, accelerating the branch-and-bound max-cardinality
search. Our method is demonstrated to match the performance of full JC methods
at significantly-reduced computational cost, facilitating robust object-based
loop-closure over large SLAM problems.Comment: Accepted to IEEE International Conference on Robotics and Automation
(ICRA) 201
AutoML: A new methodology to automate data pre-processing pipelines
It is well known that we are living in the Big Data Era. Indeed, the exponential growth of Internet of Things, Web of Things and Pervasive Computing systems greatly increased the amount of stored data. Thanks to the availability of data, the figure of the Data Scientist has become one of the most sought, because he is capable of transforming data, performing analysis on it, and applying Machine Learning techniques to improve the business decisions of companies. Yet, Data Scientists do not scale. It is almost impossible to balance their number and the required effort to analyze the increasingly growing sizes of available data. Furthermore, today more and more non-experts use Machine Learning tools to perform data analysis but they do not have the required knowledge. To this end, tools that help them throughout the Machine Learning process have been developed and are typically referred to as AutoML tools. However, even with the presence of such tools, raw data (i.e., without being pre-processed) are rarely ready to be consumed, and generally perform poorly when consumed in a raw form.
A pre-processing phase (i.e., application of a set of transformations), which improves the quality of the data and makes it suitable for algorithms is usually required.
Most of AutoML tools do not consider this preliminary part, even though it has already shown to improve the final performance. Moreover, there exist a few works that actually support pre-processing, but they provide just the application of a fixed series of transformations, decided a priori, not considering the nature of the data, the used algorithm, or simply that the order of the transformations could affect the final result. In this thesis we propose a new methodology that allows to provide a series of pre-processing transformations according to the specific presented case. Our approach analyzes the nature of the data, the algorithm we intend to use, and the impact that the order of transformations could have
Uncertainty in Natural Language Generation: From Theory to Applications
Recent advances of powerful Language Models have allowed Natural Language
Generation (NLG) to emerge as an important technology that can not only perform
traditional tasks like summarisation or translation, but also serve as a
natural language interface to a variety of applications. As such, it is crucial
that NLG systems are trustworthy and reliable, for example by indicating when
they are likely to be wrong; and supporting multiple views, backgrounds and
writing styles -- reflecting diverse human sub-populations. In this paper, we
argue that a principled treatment of uncertainty can assist in creating systems
and evaluation protocols better aligned with these goals. We first present the
fundamental theory, frameworks and vocabulary required to represent
uncertainty. We then characterise the main sources of uncertainty in NLG from a
linguistic perspective, and propose a two-dimensional taxonomy that is more
informative and faithful than the popular aleatoric/epistemic dichotomy.
Finally, we move from theory to applications and highlight exciting research
directions that exploit uncertainty to power decoding, controllable generation,
self-assessment, selective answering, active learning and more
Brain-Inspired Computational Intelligence via Predictive Coding
Artificial intelligence (AI) is rapidly becoming one of the key technologies
of this century. The majority of results in AI thus far have been achieved
using deep neural networks trained with the error backpropagation learning
algorithm. However, the ubiquitous adoption of this approach has highlighted
some important limitations such as substantial computational cost, difficulty
in quantifying uncertainty, lack of robustness, unreliability, and biological
implausibility. It is possible that addressing these limitations may require
schemes that are inspired and guided by neuroscience theories. One such theory,
called predictive coding (PC), has shown promising performance in machine
intelligence tasks, exhibiting exciting properties that make it potentially
valuable for the machine learning community: PC can model information
processing in different brain areas, can be used in cognitive control and
robotics, and has a solid mathematical grounding in variational inference,
offering a powerful inversion scheme for a specific class of continuous-state
generative models. With the hope of foregrounding research in this direction,
we survey the literature that has contributed to this perspective, highlighting
the many ways that PC might play a role in the future of machine learning and
computational intelligence at large.Comment: 37 Pages, 9 Figure
Decentralized Narrowband and Wideband Spectrum Sensing with Correlated Observations
This dissertation evaluates the utility of several approaches to the design of good distributed sensing systems for both narrowband and wideband spectrum sensing problems with correlated sensor observations
- …