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  • PhD Defense - Sahil Garg

    Fri, May 03, 2019 @ 03:00 PM - 05:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Candidate:
    Sahil Garg

    DateTime: 5/3 from 3pm to 5pm
    Location: GFS 213.

    Aram Galstyan (chair)
    Kevin Knight
    Greg Ver Steeg
    Roger Georges Ghanem
    Irina Rish

    Dissertation Title: Hashcode Representations of Natural Language for Relation Extraction

    This thesis studies the problem of identifying and extracting relationships between biological entities from the text of scientific papers. For the relation extraction task, state-of-the-art performance has been achieved by classification methods based on convolutional kernels which facilitate sophisticated reasoning on natural language text using structural similarities between sentences and/or their parse trees. Despite their success, however, kernel-based methods are difficult to customize and computationally expensive to scale to large datasets. We address the first problem by proposing a nonstationary extension to the conventional convolutional kernels for improved expressiveness and flexibility. For scalability, we propose to employ kernelized locality sensitive hashcodes as explicit representations of natural language structures, which can be used as feature-vector inputs to arbitrary classification methods. We propose a theoretically justified method for optimizing the representations that is based on approximate and efficient maximization of the mutual information between the hashcodes and their class labels. We evaluate the proposed approach on multiple biomedical relation extraction datasets, and observe significant and robust improvements in accuracy over state-of-the-art classifiers, along with drastic orders-of-magnitude speedup compared to conventional kernel methods.
    Finally, we introduce a nearly-unsupervised framework for learning kernel- or neural- hashcode representations. We define an information-theoretic objective which leverages both labeled and unlabeled data points for fine-grained optimization of each hash function, and propose a greedy algorithm for maximizing that objective. This novel learning paradigm is beneficial for building hashcode representations generalizing from a training set to a test set. We conduct a thorough experimental evaluation on the relation extraction datasets, and demonstrate that the proposed extension leads to superior accuracies with respect to state-of-the-art supervised and semi-supervised approaches, such as variational autoencoders and adversarial neural networks. An added benefit of the proposed representation learning technique is that it is easily parallelizable, interpretable, and owing to its generality, applicable to a wide range of NLP problems.

    Location: Grace Ford Salvatori Hall Of Letters, Arts & Sciences (GFS) - 213

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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  • PhD Defense - Chao Yang

    Tue, May 07, 2019 @ 01:00 PM - 03:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    Ph.D. Defense - Chao Yang
    Tue, May 7th, 2019
    1:00 pm - 3:00 pm
    Location: SAL 322


    PhD Candidate: Chao Yang
    Date, Time, and Location: Tuesday, May 7th, 2019 at 1pm in SAL 322
    Committee: Prof. C.-C. Jay Kuo, Prof. Keith Jenkins, and Prof. Jernej Barbic

    In the thesis, we tackle the problem of translating faces and bodies between different identities without paired training data: we cannot directly train a translation module using supervised signals in this case. Instead, we propose to train a conditional variational auto-encoder (CVAE) to disentangle different latent factors such as identity and expressions. In order to achieve effective disentanglement, we further use multi-view information such as keypoints and facial landmarks to train multiple CVAEs. By relying on these simplified representations of the data we are using a more easily disentangled representation to guide the disentanglement of image itself. Experiments demonstrate the effectiveness of our method in multiple face and body datasets. We also show that our model is a more robust image classifier and adversarial example detector comparing with traditional multi-class neural networks.

    To address the issue of scaling to new identities and also generate better-quality results, we further propose an alternative approach that uses self-supervised learning based on StyleGAN to factorize out different attributes of face images, such as hair color, facial expressions, skin color, and others. Using pre-trained StyleGAN combined with iterative style inference we can easily manipulate the facial expressions or combine the facial expressions of any two people, without the need of training a specific new model for each of the identity involved. This is one of the first scalable and high-quality approach for generating DeepFake data, which serves as a critical first step to learn a more robust and general classifier against adversarial examples.

    Location: Henry Salvatori Computer Science Center (SAL) - 322

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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  • PhD Defense - Zahaib Akhtar

    Wed, May 29, 2019 @ 10:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Candidate: Zahaib Akhtar
    Wednesday May 29th, 2019
    10:00a.m. - 12:00
    Location: SAL 2nd Floor Conference Room
    Committee: Antonio Ortega, Barath Raghavan, Ramesh Govindan

    Understanding and Optimizing Video Delivery

    In this work, we strive to understand and optimize the performance of systems which serve video over the Internet. Towards this end, we make the following three contributions. First, we perform a systematic study of the video management plane -- the systems which form the video delivery pipeline. Second, we propose an approach called Oboe to improve the performance of client side video players by tuning Adaptive Bitrate Algorithms to the characteristics of a network. A range of different algorithm when augmented with Oboe improve their performance by up to 25%. Finally, we propose a caching algorithm called AVC which is specifically tailored for video workload. AVC outperforms state of the art caching algorithms by exploiting various properties of adaptive bitrate video. In particular, a LRU cache requires 3x the memory used by AVC to match its performance.

    Location: Henry Salvatori Computer Science Center (SAL) - 213

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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