Phil 8.1.19

7:00 – 3:30 ASRC GEOS

  • Cancel service at Bob’s – done
  • Scan hotel receipt and fill out expense report
  • Write up some USPTO thoughts – done
  • School reimbursement and approval for 899 – forms filled out, waiting for signatures
  • Write down thoughts on inhibition and excitation in groups. Basically, when a group is engaged in discussion, some links are excitatory – a small group will engage in discussion, while others participate less or not at all – they are inhibited. These kind of discussions are almost always mediated by an explicit or implicit leader. The consensus that develops is greatly influenced by who is excited and who is inhibited.
  • July progress email – done
  • Dissertation
    • Work on flowchart(s)
  • Distributed Memory and the Representation of General and Specific Information
    • We describe a distributed model of information processing and memory and apply it to the representation of general and specific information. The model consists of a large number of simple processing elements which send excitatory and inhibitory signals to each other via modifiable connections. Information processing is thought of as the process whereby patterns of activation are formed over the units in the model through their excitatory and inhibitory interactions. The memory trace of a processing event is the change or increment to the strengths of the interconnections that results from the processing event. The traces of separate events are superimposed on each other in the values of the connection strengths that result from the entire set of traces stored in the memory. The model is applied to a number of findings related to the question of whether we store abstract representations or an enumeration of specific experiences in memory. The model simulates the results of a number of important experiments which have been taken as evidence for the enumeration of specific experiences. At the same time, it shows how the functional equivalent of abstract representations- prototypes, logogens, and even rules-can emerge from the superposition of traces of specific experiences, when the conditions are right for this to happen. In essence, the model captures the structure present in a set of input patterns; thus, it behaves as though it had learned prototypes or rules, to the extent that the structure of the environment it has learned about can be captured by describing it in terms of these abstractions.
  • Leveraging Meta Information in Short Text Aggregation
    • Analysing topics in short texts (e.g., tweets and new headings) is a challenging task because short texts often contain insufficient word co-occurrence information, which is important to learn good topics in conventional topic topics. To deal with the insufficiency, we propose a generative model that aggregates short texts into clusters by leveraging the associated meta information. Our model can generate more interpretable topics as well as document clusters. We develop an effective Gibbs sampling algorithm favoured by the fully local conjugacy in the model. Extensive experiments demonstrate that our model achieves better performance in terms of document clustering and topic coherence.

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