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PRODID:-//Cornell U. Department of Computer Science//Brown Bag Seminar//EN
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SUMMARY:Brown bag: Geoff Pleiss\, Vishal Shrivastav\, Laure Thompson
DESCRIPTION:Title: Student Colloquium\nSpeaker: Geoff Pleiss\, Vishal
	 Shrivastav\, Laure Thompson\nAbstract: From n=1\,000 to n=1\,000\,000:
	 Scaling Up Gaussian Processes Inference with Matrix Multiplication and
	 GPU Acceleration - Geoff Pleiss\nGaussian processes (GPs) are powerful
	 machine learning models-offering well-calibrated uncertainty estimates\,
	 interpretable predictions\, and the ability to encode prior knowledge.
	 Despite these desirable properties\, GPs are typically not applied to
	 datasets with more than a few thousand data points-in part because of an
	 inference procedure that requires matrix inverses\, determinants\, and
	 other expensive operations. In this talk\, I will discuss how my
	 collaborators and I were able to scale GPs to datasets with over 1
	 million points\, without making any simplifying assumptions. Taking
	 inspiration from neural network libraries\, we constrained ourselves to
	 writing a GP inference algorithm that only used matrix multiplication
	 and other linear operations-procedures that are extremely amenable to
	 parallelization\, GPU acceleration\, and distributed computing. The
	 resulting algorithm\, Blackbox Matrix-Matrix Inference (BBMM)\, is up to
	 100x faster than existing inference procedures and scales to datasets
	 that are 2 orders of magnitude larger than what has previously been
	 reported.\n\nBuilding High-speed Datacenter Networks in the Post-Moore's
	 Law Era - Vishal Shrivastav\nWith the slowdown in Moore's law and the
	 end of Dennard scaling\, general-purpose CPUs and packet switches have
	 hit a fundamental performance wall. However\, the bandwidth demand
	 within datacenters keeps growing exponentially: Applications keep
	 getting more distributed and resources (e.g.\, storage) keep getting
	 disaggregated demanding more bandwidth. In this talk\, I will discuss
	 how my research attempts to bridge this gap for next-generation
	 datacenter networks. To overcome the limitations of general-purpose
	 CPUs\, my research argues for domain-specific architectures for network
	 processing. I will start by briefly introducing several domain-specific
	 hardware architectures that I have proposed over the course of my PhD\,
	 targeted at improving a wide-range of core network functions including
	 packet scheduling\, packet processing\, congestion control\, and network
	 time synchronization.\nTo accommodate the exponential demand for network
	 bandwidth\, in the talk I will focus on how my research attempts to
	 overcome the fundamental limitations of packet switching within
	 datacenters. I will describe a new approach called Shoal that is the
	 first end-to-end network design for a fast circuit-switched network. I
	 will conclude my talk by discussing how Shoal takes us a giant step
	 towards realizing the idealistic goal of a datacenter switching fabric
	 that could support practically unlimited bandwidth at low power\, low
	 cost and high performance.\n\nUnderstanding and Directing What Models
	 Learn - Laure Thompson\nMachine learning and statistical methods\, such
	 as unsupervised semantic models\, are popular and useful techniques for
	 making massive digital collections more explorable and analyzable. But
	 what underlying patterns do these models actually learn\, and which
	 patterns are they most likely to repeatedly learn? Moreover\, how might
	 we direct what these models learn so that they are useful to a wider
	 range of scholarly inquiry? While it might be useful to organize texts
	 by authors\, learning this structure is seldom useful when already known
	 and can be problematic if it is mischaracterized as a cross-cutting
	 pattern. In this talk\, I will focus on a specific problem: discuss my
	 recent work on measuring and mitigating topic-metadata correlation in
	 topic models.\n
LOCATION:Gates 122
UID:2019-11-05
STATUS:TENTATIVE
DTSTART:20191105T170000Z
DTEND:20191105T180000Z
LAST-MODIFIED:20191101T183245Z
ORGANIZER;CN=Jonathan Shi:http://www.cs.cornell.edu/~jshi/brownbag/
DTSTAMP:20260408T121929Z
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