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X-WR-CALNAME:Scalable Analytics Institute (ScAi)
X-ORIGINAL-URL:https://scai.cs.ucla.edu
X-WR-CALDESC:Events for Scalable Analytics Institute (ScAi)
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DTSTART;TZID=America/Los_Angeles:20140307T120000
DTEND;TZID=America/Los_Angeles:20140307T130000
DTSTAMP:20261012T110939
CREATED:20140304T192628Z
LAST-MODIFIED:20140304T193245Z
UID:160-1394193600-1394197200@scai.cs.ucla.edu
SUMMARY:Hypothesis Exploration across Disciplines
DESCRIPTION:Speaker: Prof. Stott Parker \nAbstract: \nA consequence of the abundance of data of all forms is that scientific research efforts are increasingly cutting across disciplines. Interdisciplinary research is difficult for many reasons\, but among these are the difficulties of analyzing heterogeneous data and the lack of methods for collaborative construction of hypotheses. This is particularly true in fields like neuroscience\, where the data is complex and ranges over many orders of magnitude in scale — and no single individual can hope to master it all. \nIn this talk I describe a system for exploration of hypotheses in phenotype data\, implemented with a database obtained from several studies at UCLA. ViVA is a web-based system for analyzing hypotheses about variance structure\, permitting exploratory analysis of GLMs. It permits visual identification of phenotype profiles (patterns of values across phenotypes) that characterize groups (subpopulations)\, and includes a variety of methods for visualization of variance. Visualization supports interdisciplinary collaboration\, and enables screening and refinement of hypotheses about sets of phenotypes. With several examples we illustrate how this approach supports “natural selection” on a pool of hypotheses\, and permits deeper understanding of the statistical architecture of the data. \nViVA was designed for investigation of data concerning the biological bases of traits such as memory and response inhibition phenotypes — to explore whether they can aid in moving from traditional categorical approaches for psychiatric syndromes towards more quantitative approaches based on large-scale analysis of the space of human variation. The hypotheses and data are increasingly trans-disciplinary and sophisticated\, and the impact of better methods can be enormous.
URL:https://scai.cs.ucla.edu/event-hypothesis-exploration-across-disciplines.html
LOCATION:3551P Boelter Hall
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DTSTART;TZID=America/Los_Angeles:20140314T120000
DTEND;TZID=America/Los_Angeles:20140314T130000
DTSTAMP:20261012T110939
CREATED:20140304T193215Z
LAST-MODIFIED:20140304T193215Z
UID:163-1394798400-1394802000@scai.cs.ucla.edu
SUMMARY:Computer Vision meets Big Data: Complexity and Compositionality
DESCRIPTION:Speaker: Prof. Alan L. Yuille \nAbstract: \nBig data arises naturally in computer vision because of the enormous number and variety of images and the large range of visual tasks that we want to perform on them. Computer vision researchers must pay increasingly attention to complexity issues as they develop algorithms that work on large image datasets. This talk has two parts. The first part describes practical issues that arise when working with large datasets such as Pascal and ImageNet. These include efficient algorithms\, parallel implementations (e.g.\, GPUs)\, and special purpose hardware. The second part describes theoretical work that addresses arguably the fundamental problem of vision — how can a visual system store (represent)\, rapidly access (do inference)\, and learn the enormous number and variety of objects — and configurations of objects — that occur in the world? We propose and analysis a simplified hierarchical compositional model that can address many of these issues\, and which may relate to the structure of the human visual system.
URL:https://scai.cs.ucla.edu/event-computer-vision-meets-big-data-complexity-and-compositionality.html
LOCATION:3551P Boelter Hall
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