Signal Processing - November 2017 - 8
PANEL AND FORUM
Yonina C. Eldar, Alfred O. Hero III, Li Deng, Jeff Fessler,
Jelena Kovacˇevic´, H. Vincent Poor, and Steve Young
Challenges and Open Problems in Signal Processing:
Panel Discussion Summary from ICASSP 2017
T
his column summarizes the panel
on open problems in signal process-
ing, which took place on 5 March
2017 at the International Conference on
Acoustics, Speech, and Signal Process-
ing (ICASSP) in New Orleans, Louisi-
ana. The goal of the panel was to draw
attention to some of the challenges and
open problems in various areas of signal
processing and generate discussion on
future research areas that can be of ma-
jor significance and impact in signal
processing. Five leading experts repre-
senting diverse areas within signal pro-
cessing made up the panel:
■■ Li Deng, Microsoft: machine learning
■■ Jeff Fessler, the University of Michigan:
medical imaging
■■ Jelena Kovacˇevic´, Carnegie Mellon
University: graph signal processing
■■ H. Vincent Poor, Princeton Univer-
sity: wireless communication
■■ Steve Young, the University of
Cam-bridge: speech and language
processing.
It was organized and moderated by Yo-
nina Eldar from the Technion and Al-
fred O. Hero III from the University
of Michigan.
The panel drew a very large crowd
and stimulated a vibrant discussion on
directions, trends, and challenges of sig-
nal processing in the 21st century and
in the era of big data. In this column,
we summarize the main points raised by
the panelists and the audience in each of
Digital Object Identifier 10.1109/MSP.2017.2743842
Date of publication: 13 November 2017
8
the aforementioned topics. Our goal and
hope is to further the discussion on some
of the main challenges and opportuni-
ties for signal processing in the coming
years and to highlight areas where, as a
community, working and collaborating
together, we may have impact on theory,
applications, and education.
Next, we summarize open problems
in the previously mentioned areas, high-
lighted by the participants: open problems
in machine learning, medical imaging,
graph signal processing, physical layer
wireless communications, and speech
and language processing. A common
cross-cutting theme that emerged was the
opportunity to improve performance by
the better integration of accurate physical
models into state-of-the-art -algorithms.
Open problems in machine learning
Machine learning aims to give comput-
ers the ability to learn by exploiting data
instead of being explicitly programmed.
There are many approaches in machine
learning, including support vector ma-
chines, decision-tree learning, artificial
neural networks, Bayesian networks,
genetic algorithms, rule-based learning,
and inductive logical programming, among
others [3]. In recent years, the fastest
growing area of machine learning comes
from neural networks and related gen-
erative models, where carefully design--
ed hierarchies are built into the overall
machine-learning models to form mul-
tiple layers of latent representations that
disentangle the confounding factors and
complexity in the raw data. This type of
IEEE SIGNAL PROCESSING MAGAZINE
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November 2017
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hierarchical model and the associated
machine-learning algorithms are called
deep learning [1], [2], which represents
the most recent and influential advance
in machine learning over the past decade.
The first successful application of deep
learning in real-world tasks came from
speech recognition in our signal process-
ing community and was published in this
magazine [13], followed quickly with
computer vision, natural language pro-
cessing, robotics, speech synthesis, and
image rendering [2].
Despite impressive empirical suc-
cesses of deep learning and other ma-
chine-learning approaches, many open
problems remain to be solved. Current
deep-learning methods typically lack
interpretability, in contrast to traditional
machine-learning techniques based on
linear models. In a number of applica-
tions, deep-learning methods achieve
recognition accuracy close to or exceed-
ing that of humans, but they require
considerably more training data, power
consumption, and computing re--sources
than humans. In addition, although ac-
curacy results are often statistically im-
pressive, they are often unreliable on
an individual basis. Finally, most of the
current deep-learning models have no
reasoning and explaining capabilities,
making them vulnerable to disastrous
failures or attacks without the ability to
foresee and thus to prevent them.
To overcome these challenges, both
fundamental and applied research is
needed. One potential breakthrough
in machine learning is in developing
Table of Contents for the Digital Edition of Signal Processing - November 2017
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