12 Matching Annotations
  1. Jul 2020
  2. Apr 2020
    1. Graphically, interactions can be seen as non-parallel lines connecting means when we are working with the simple two-factor factorial with 2 levels of each main effect (adapted from Zar, H. Biostatistical Analysis, 5th Ed., 1999). Remember interactions are referring to the failure of a response variable to one factor to be the same at different levels of another factor. So when lines are parallel the response is the same. In the plots below you will see parallel lines as a consistent feature in all of the plots with no interaction. In plots depicting interactions, you notice that the lines cross (or would cross if the lines kept going).

      Main and interaction effects - graphs

  3. Feb 2020
  4. Apr 2019
  5. Jan 2019
    1. Explanatory Graphs for CNNs

      Q Zhang 在知乎上亲自解答关于 Explanatory Graphs 的技术细节和研究理念~ http://t.cn/EqfQbAW [赞]

  6. Dec 2018
    1. Inflation-adjusted Textbook Pain Multiplier for Decision-Makers

      Analysis and solutions to better convey the economic impact of rising textbook costs.

  7. Nov 2018
    1. One way to identify cycles is to build a dependency graph representing all services in the system and all RPCs exchanged among them. Begin building the graph by putting each service on a node of the graph and drawing directed edges to represent the outgoing RPCs. Once all services are placed in the graph, the existing dependency cycles can be identified using common algorithms such as finding a topological sorting via a depth-first search. If no cycles are found, that means the services' dependencies can be represented by a DAG (directed acyclic graph).
    1. Creating KGs is not trivial.

      This applies to universal KG in particular. Domain specific KGs can have any level of complexity - can they still be called knowledge graphs then?

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  8. Oct 2017
  9. Jan 2017
  10. Feb 2015