[Colloquium] Dziedzic/Dissertation Defense/Jul 17, 2020

[Rene Noyola]

This is a reminder announcement of Dziedzic/Dissertation Defense. 
    
July 17, 2020 at 9:00 AM via Zoom.

Join Zoom Meeting
    https://uchicagostudents.zoom.us/j/94077870948?pwd=NGh4K200RXJPY3JKaFNpanlJUENqZz09
    
    Meeting ID: 940 7787 0948 Password: AdJuWe9804
    
           Department of Computer Science/The University of Chicago
    
                         *** Dissertation Defense ***
  
    
    Candidate:  Adam Dziedzic
    
    Date:  Friday, July 17, 2020
    
    Time:  9:00 AM
    
    Place:  remotely via Zoom
    
    Title: Input and Model Compression for Adaptive and Robust Neural
    Networks
    
    Abstract:
    The convolutional layers are core building blocks of neural network
    architectures. In general, a convolutional filter applies to the
    entire frequency spectrum of the input data. Our band-limiting method
    artificially constrains the frequency spectra of these filters and
    data during training and inference. The frequency-domain constraints
    apply to both the feed-forward and back-propagation steps.
    Experimental results confirm that Convolutional Neural Networks (CNNs)
    are resilient to this compression scheme and show that CNNs learn to
    leverage lower-frequency components. In particular, the band-limited
    training can effectively control resource usage (GPU and memory). Our
    method with 50% compression in the frequency domain results in only a
    1.5% drop in accuracy for the ReNet-18 model trained on CIFAR-10 data
    while reducing the GPU memory usage by 40% and the computation time by
    30% in comparison to their full-spectra counterparts. The models
    trained with band-limited layers retain high prediction accuracy and
    require no modification to existing training algorithms or neural
    network architectures, unlike other compression schemes. We apply the
    band-limited models to develop a new solution for a fair wireless
    co-existence between Wi-Fi access points and LTE-U (Long Term
    Evolution-Unlicensed) base stations.
    
    Band-limited models are also robust to noisy inputs and naturally
    extend to perturbation-based defenses that improve robustness to
    adversarial attacks. The input perturbation defenses, whether random
    or deterministic, are equivalent in their efficacy. However, the
    perturbation based defenses offer almost no robustness to adaptive
    attacks unless these perturbations are observed during training and a
    tuned sequence of noise layers is placed across a network. From the
    theoretical perspective, adversarial examples in a close neighborhood
    of original inputs show an elevated sensitivity to perturbations in
    first and second-order analyses. From the application perspective, we
    explore the out-of-distribution robustness of models in terms of their
    generalization and detection of anomalous inputs in the NLP (Natural
    Language Processing) domain.
    
    Adam's advisor is Prof. Sanjay Krishnan
    
    Login to the Computer Science Department website for details,
    including a draft copy of the dissertation:
    
     https://newtraell.cs.uchicago.edu/phd/phd_announcements#ady
    
Best,
 
Rene
---
Rene Noyola 
Executive Assistant to the Dept. Manager
Computer Science Department
5730 S. Ellis - Room 216
Chicago, IL 60637
(773) 702-0723
rnoyola at uchicago.edu

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