Tutorials have been officially announced. This year we are glad to present the following tutorials: Graph Filters with Applications to Distributed Optimization and Neural Networks Distributed and Efficient Deep Learning Adversarial Robustness of Deep Learning Read more…
Welcome to ICASSP 2020
On behalf of the IEEE Signal Processing Society, the organizing committee is delighted to invite you to the 45th International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020, to be held in Barcelona, Spain between May 4 and May 8, 2020!
ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The 2020 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world.
ICASSP 2020 will feature a mix of national and international speakers, tutorials, workshops and exhibits. Featuring contemporary research, highly regarded presenters and a focus on translating research into practice the conference is sure to be an exciting event for all who attend.
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Deep Representation Learning
A crucial ingredient of deep learning is that of learning representations, more specifically with the objective to discover higher-level representations which capture and disentangle explanatory factors. This is a very ambitious goal and current state-of-the-art techniques still fall short, often capturing mostly superficial features of the data, which leaves them vulnerable to adversarial attacks and insufficient out-of-distribution robustness. This talk will review these original objectives, supervised and unsupervised approaches, and outline research ideas towards better representation learning.
From compressed sensing to deep learning: tasks, structures, and models
The famous Shannon-Nyquist theorem has become a landmark in the development of digital signal and image processing. However, in many modern applications, the signal bandwidths have increased tremendously, while the acquisition capabilities have not scaled sufficiently fast. Consequently, conversion to digital has become a serious bottleneck. In this talk, we present a framework for sampling and processing a large class of wideband analog signals at rates far below Nyquist in space, time and frequency, which allows to dramatically reduce the number of antennas, sampling rates and band occupancy.
Multiantenna Precoding in Wireless Communication Systems
The advent of spatial processing in multiantenna wireless communications has transformed the design of mobile networks allowing us to meet the tremendous demands for data and services in mobile applications. Signal processing techniques implemented in base-band transceivers enables the efficient exploitation of radio spectrum, increased gain to improve coverage, more reliable and secure transmissions and improved energy efficiency. We will focus on the challenges of spatial precoding techniques implemented on the transmit side of wireless systems. Early developments on transmit beamforming and spatial division multiple access will be reviewed as well as space-time coding and more recent developments on symbol-level precoding.