Difference between revisions of "INFOCOMP-2017 Venice"
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I'm attending INFOCOMP17, a conference where I have already presented several papers in the past. I presented the paper detailing the work done by Kyra Gan, Class of 2017, on [http://cs.smith.edu/classwiki/index.php/Data_Visualization_Transit_Map_of_the_Spread_of_an_Infectious_Disease_in_a_Closed_Student_Population the data visualization of the spread of a virus]. The paper is available [http://www.science.smith.edu/dftwiki/images/5/5a/DataVisAgentBasedModelingVirusSpread_GanThiebaut2017.pdf here]. Kyra is now (as of June 2017) a graduate student at CMU's Tepper School of Business. | I'm attending INFOCOMP17, a conference where I have already presented several papers in the past. I presented the paper detailing the work done by Kyra Gan, Class of 2017, on [http://cs.smith.edu/classwiki/index.php/Data_Visualization_Transit_Map_of_the_Spread_of_an_Infectious_Disease_in_a_Closed_Student_Population the data visualization of the spread of a virus]. The paper is available [http://www.science.smith.edu/dftwiki/images/5/5a/DataVisAgentBasedModelingVirusSpread_GanThiebaut2017.pdf here]. Kyra is now (as of June 2017) a graduate student at CMU's Tepper School of Business. | ||
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One quote I heard at a panel on Tuesday expressed quite a bit my recent exploration of Neural Nets (NN), and in particular the use of TensorFlow and Long-Short-Term-Memory based network to predict time series: "Neural Nets are an art." It was said by Alexander Troussov, of IBM, Dublin. Getting NN to converge requires tweaking, and a lot of trials, playing with the number of layers, the width of layers, the type of cells, the output functions, etc. | One quote I heard at a panel on Tuesday expressed quite a bit my recent exploration of Neural Nets (NN), and in particular the use of TensorFlow and Long-Short-Term-Memory based network to predict time series: "Neural Nets are an art." It was said by Alexander Troussov, of IBM, Dublin. Getting NN to converge requires tweaking, and a lot of trials, playing with the number of layers, the width of layers, the type of cells, the output functions, etc. | ||
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| − | [[Image:TakisStella.jpg|center| | + | I was also happy to meet colleagues from Wellesley College, Takis Metaxa, and Stella Kakavouli, whose two daughters, Artemis and Danaë, are or have attended Smith, in different ways (regular student and during a junior-semester-abroad). Takis presented a paper titled "Manipulation of Search Engine Results during the 2016 US Congressional Elections," which covers how the algorithms used by various search engines to return query results had been compromised, and tricked. Very interesting and a-propos research. |
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Latest revision as of 10:05, 11 July 2017
--D. Thiebaut (talk) 07:17, 26 June 2017 (EDT)
INFOCOMP17Venice, Italy, June 26-19, 2017
I'm attending INFOCOMP17, a conference where I have already presented several papers in the past. I presented the paper detailing the work done by Kyra Gan, Class of 2017, on the data visualization of the spread of a virus. The paper is available here. Kyra is now (as of June 2017) a graduate student at CMU's Tepper School of Business.
One quote I heard at a panel on Tuesday expressed quite a bit my recent exploration of Neural Nets (NN), and in particular the use of TensorFlow and Long-Short-Term-Memory based network to predict time series: "Neural Nets are an art." It was said by Alexander Troussov, of IBM, Dublin. Getting NN to converge requires tweaking, and a lot of trials, playing with the number of layers, the width of layers, the type of cells, the output functions, etc.
I was also happy to meet colleagues from Wellesley College, Takis Metaxa, and Stella Kakavouli, whose two daughters, Artemis and Danaë, are or have attended Smith, in different ways (regular student and during a junior-semester-abroad). Takis presented a paper titled "Manipulation of Search Engine Results during the 2016 US Congressional Elections," which covers how the algorithms used by various search engines to return query results had been compromised, and tricked. Very interesting and a-propos research.
