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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Random Automata Networks: Why Playing Dice Is Not A Vice, Christof Teuscher
Random Automata Networks: Why Playing Dice Is Not A Vice, Christof Teuscher
Systems Science Friday Noon Seminar Series
Random automata networks consist of a set of simple compute nodes interacting with each other. In this generic model, one or multiple model parameters, such as the the node interactions and/or the compute functions, are chosen at random. Random Boolean Networks (RBNs) are a particular case of discrete dynamical automata networks where both time and states are discrete. While traditional RBNs are generally credited to Stuart Kauffman (1969), who introduced them as simplified models of gene regulation, Alan Turing proposed unorganized machines as early as 1948. In this talk I will start with Alan Turing's early work on unorganized machines, …
Digitally Optimizing "Smart" Photovoltaics, Nicholas M. Christensen
Digitally Optimizing "Smart" Photovoltaics, Nicholas M. Christensen
Von Braun Symposium Student Posters
No abstract provided.
My Point Of View, Michael L. Nelson
My Point Of View, Michael L. Nelson
Computer Science Presentations
PDF of a powerpoint presentation from the Web Archiving Cooperative (WAC) Meeting, Stanford University, September 9, 2010. Also available on Slideshare.
Biologically Inspired Computing: The Darpa Synapse Program & The Hierarchical Temporal Memory, Dan Hammerstrom
Biologically Inspired Computing: The Darpa Synapse Program & The Hierarchical Temporal Memory, Dan Hammerstrom
Systems Science Friday Noon Seminar Series
This presentation provides an update on biologically inspired computation. In particular, it focuses on two important developments in this area, the DARPA SyNAPSE program (Systems of Neuromorphic Adaptive Plastic Scalable Electronics) and the HTM (Hierarchical Temporal Memory) being developed by Numenta.
The SyNAPSE Program’s ultimate goal is to build a low-power, compact electronic chip combining novel analog circuit design and a neuroscience-inspired architecture that can address a wide range of cognitive abilities: perception, planning, decision making and motor control. According to DARPA program manager Todd Hylton, “Our research progress in this area is unprecedented, No suitable electronic synaptic device that …
Understanding Classification Decisions For Object Detection, Will Landecker, Michael David Thomure, Melanie Mitchell
Understanding Classification Decisions For Object Detection, Will Landecker, Michael David Thomure, Melanie Mitchell
Systems Science Friday Noon Seminar Series
Computer vision systems are traditionally tested in the object detection paradigm. In these experiments, a vision system is asked whether or not a specific object--for example an animal--occurs in a given image. A system that often answers correctly is said to be very accurate. In this talk, we will discuss some ambiguity that exists in this measure of accuracy. We will also propose a new measure of object-detection accuracy that addresses some of this ambiguity, and apply this measure to the hierarchical "standard model" of visual cortex.