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Events for August 31, 2017

  • EE 598 Computer Engineering Seminar Series

    Thu, Aug 31, 2017 @ 02:00 PM - 03:15 PM

    Ming Hsieh Department of Electrical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Rakesh Kumar, University of Illinois at Urbana Champaign

    Talk Title: Ultra Low Power Computing in the IoT Era

    Series: EE 598 Computer Engineering Seminar Series

    Abstract: Wearables, sensors, and Internet of Things (IoT) arguably represent the next frontier of computing. They will be characterized by extremely low power and area requirements. In our recent research, we asked the question: are there opportunities for power and area reduction that are unique to these emerging computing platforms. We answered the question in the affirmative and developed several techniques that appear to be very effective. In this talk, I will focus one such technique--symbolic hardware-software co-analysis--that is applicable over a wide class of applications. Through a novel symbolic execution-based approach, we can determine for a given application the gates in the hardware that the application is guaranteed to not touch. This information can then be used to determine application-specific Vmin, determine application-specific peak power, and, build bespoke processors customized to a given application. If time permits, I will also discuss how architectural ideas such bit serial processors and k-hot pipelining may become promising for the IoT applications.

    Biography: Rakesh Kumar is an Associate Professor in the Electrical and Computer Engineering Department at the University of Illinois at Urbana Champaign and a Co-Founder and Chief Architect at Hyperion Core, Inc. He has made contributions in the area of processor design and memory system design that have directly impacted industry and state-of-art. His current research interests are in computer architecture, low power and error resilient computer systems, and approximate computing. He has a B-Tech from IIT Kharagpur and a PhD from University of California at San Diego. He is often seen at a restaurant or hanging out with his very active four-year old.

    Host: Xuehai Qian, x04459, xuehai.qian@usc.edu

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

    Audiences: Everyone Is Invited

    Posted By: Gerrielyn Ramos

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  • THURSDAY TALKS: NL Seminars-1 Recurrent Neural Networks as Weighted Language Recognizers 2 Gloss-to-English: Improving Low Resource Language Translation Using Alignment Tables

    Thu, Aug 31, 2017 @ 03:00 PM - 04:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Yining Chen and Sasha Mayn , USC/ISI Interns

    Talk Title: THURSDAY TALKS: 1 Recurrent Neural Networks as Weighted Language Recognizers 2 Gloss-to-English: Improving Low Resource Language Translation Using Alignment Tables

    Series: Natural Language Seminar

    Abstract: 1. We investigate properties of a simple recurrent neural network RNN as a formal device for recognizing weighted languages. We focus on the single layer, ReLU activation, rational weight RNN with softmax, a standard form of RNN used in language processing applications. We prove that many questions one may ask about such RNNs are undecidable, including consistency, equivalence, minimization, and finding the highest weighted string. For consistent RNNs, finding the highest weighted string is decidable, although the solution can be exponentially long in the length of the input RNN encoded in binary. Limiting to solutions of polynomial length, we prove that finding the highest-weighted string for a consistent RNN is NP complete and APX hard.

    2. Neural Machine Translation has gained popularity in recent years and has been able to achieve impressive results. The only caveat is that millions of parallel sentences are needed in order to train the system properly, and in a low resource scenario that amount of data simply may not be available. This talk will discuss strategies for addressing the data scarcity problem, particularly using alignment tables to make use of parallel data from higher resource language pairs and creating synthetic in domain data.


    Biography: Yining Chen is a third year undergraduate student at Dartmouth College. She is a summer intern at ISI working with Professor Kevin Knight and Professor Jonathan May.

    Sasha Mayn is a summer intern for the ISI Natural Language Group. She is particularly interested in machine translation and language generation. Last summer Sasha interned at the PanLex Project in Berkeley, where she was responsible for preprocessing digital dictionaries and entering them into PanLex's multilingual database. This summer she has been working on improving neural machine translation strategies for low resource languages under the supervision of Jon May and Kevin Knight.


    Host: Marjan Ghazvininejad and Kevin Knight

    More Info: http://nlg.isi.edu/nl-seminar/

    Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey

    Audiences: Everyone Is Invited

    Posted By: Peter Zamar

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  • Epstein Institute Seminar, ISE 651

    Thu, Aug 31, 2017 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Georgia-Ann Klutke, National Science Foundation (NSF)

    Talk Title: Navigating NSF: Funding Opportunities for Operations Research

    Host: Prof. Suvrajeet Sen

    More Information: August 31, 2017.pdf

    Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206

    Audiences: Everyone Is Invited

    Posted By: Grace Owh

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  • PhD Defense: Sampling Theory for Graph Signals with Applications to Semi-supervised Learning

    Thu, Aug 31, 2017 @ 03:30 PM - 05:30 PM

    Ming Hsieh Department of Electrical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Aamir Anis, USC

    Talk Title: PhD Defense: Sampling Theory for Graph Signals with Applications to Semi-supervised Learning

    Abstract: The representation, processing and analysis of large-scale data as signals defined over graphs has drawn much interest recently. Graphs allow us to embed natural inter-connectivities between data points and exploit them during processing. As a result, graph signal processing has laid a strong foothold in various modern application domains such as machine learning, analysis of social, transportation, web and sensor networks, and even traditional areas such as image processing and video compression. Although powerful, this research area is still in its infancy. Recent efforts have therefore focused on translating well-developed tools of traditional signal processing for handling graph signals.

    An important aspect of graph signal processing is defining a notion of frequency for graph signals. A frequency domain representation for graph signals can be defined using the eigenvectors and eigenvalues of variation operators (e.g., graph Laplacian) that take into account the underlying graph connectivity. These operators can also be used to design graph spectral filters. The primary focus of our work is to develop a theory of sampling for graph signals that answers the following questions: 1. When can one recover a graph signal from its samples on a given subset of nodes of the graph? 2. What is the best choice of nodes to sample a given graph signal? Our formulation primarily works under the assumption of bandlimitedness in the graph Fourier domain, which amounts to smoothness of the signal over the graph. The techniques we employ to answer these questions are based on the introduction of special quantities called graph spectral proxies that allow our algorithms to operate in the vertex domain, thereby admitting efficient, localized implementations.

    We also explore the sampling problem in the context of designing wavelet filterbanks on graphs. This problem is fundamentally different since one needs to choose a sampling scheme jointly over multiple channels of the filterbank. We explore constraints for designing perfect reconstruction two-channel critically-sampled filterbanks with low-degree polynomial filters, and conclude that such a design is in general not possible for arbitrary graphs. This leads us to propose an efficient technique for designing a critical sampling scheme that, given pre-designed filters, aims to minimize the overall reconstruction error of the filterbank. We also explore M-channel filterbanks over M-block cyclic graphs (that are natural extensions of bipartite graphs), and propose a tree-structured design in a simpler setting when M is a power of 2.

    As an application, we study the graph-based semi-supervised learning problem from a sampling theory point of view. A crucial assumption here is that class labels form a smooth graph signal over a similarity graph constructed from the feature vectors. Our analysis justifies this premise by showing that in the asymptotic limit, the bandwidth (a measure of smoothness) of any class indicator signal is closely related to the geometry of the dataset. Using the sampling theory perspective, we also quantitatively show that the label complexity (i.e., the amount of labeling required for perfect prediction of unknown labels) matches its theoretical value, thereby adding to the appeal of graph-based techniques for semi-supervised learning.

    Biography: Aamir Anis received his Bachelor and Master of Technology degree in Electronics and Electrical Communication Engineering from the Indian Institute of Technology (IIT), Kharagpur, India, in 2012. He joined the Electrical Engineering department at the University of Southern California (USC), Los Angeles, in 2012, where he has been working towards a Ph.D. degree in Electrical Engineering. He has been the recipient of the Best Student Paper award at ICASSP 2014. His research interests include graph signal processing with applications in machine learning, and multimedia compression.

    Host: Dr. Antonio Ortega

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    Audiences: Everyone Is Invited

    Posted By: Gloria Halfacre

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  • McKinsey & Company Information Session

    Thu, Aug 31, 2017 @ 06:00 PM - 08:00 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions


    McKinsey is an international management consulting firm that works with leading corporations, non-profit institutions, and governments on issues of critical importance to senior management. We help our clients solve strategic, organizational, and operational problems in order to make significant and enduring improvements in their performance. What is unique about McKinsey is that we consider the people who make up the firm to be as important as the clients with whom we work. We seek to work with truly dynamic individuals -- the most talented scholars and the most compelling leaders -- to create an environment that is a great place both to learn and to have fun.

    Location: Seeley G. Mudd Building (SGM) - 101

    Audiences: All Viterbi Students

    Posted By: RTH 218 Viterbi Career Connections

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  • AOE Mason Jar Terrarium Craft Night!

    Thu, Aug 31, 2017 @ 07:00 PM - 08:00 PM

    Viterbi School of Engineering Student Organizations

    Student Activity


    Come learn more about Alpha Omega Epsilon while making mason jar terrariums! This event is free :)

    Location: Grace Ford Salvatori Hall Of Letters, Arts & Sciences (GFS) - 109

    Audiences: Undergrad

    Posted By: Alpha Omega Epsilon USC

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