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The information bottleneck method is a technique in information theory introduced by Naftali Tishby, Fernando C. Pereira, and William Bialek. [1] It is designed for finding the best tradeoff between accuracy and complexity (compression) when summarizing (e.g. clustering) a random variable X, given a joint probability distribution p(X,Y) between X and an observed relevant variable Y - and self ...
Information flow in an information theoretical context is the transfer of information from a variable to a variable in a given process.Not all flows may be desirable; for example, a system should not leak any confidential information (partially or not) to public observers—as it is a violation of privacy on an individual level, or might cause major loss on a corporate level.
Late selection models argue that information is selected after processing for meaning, as opposed to during the earlier stages of processing. [21] According to these models, all information is attended to, whether intentionally or unintentionally. Information inputs are processed equivalently, until semantic encoding and analysis can be performed.
In telecommunications, information transfer is the process of moving messages containing user information from a source to a sink via a communication channel. In this sense, information transfer is equivalent to data transmission which highlights more practical, technical aspects.
In machine learning, knowledge distillation or model distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have more knowledge capacity than small models, this capacity might not be fully utilized.
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An information flow diagram (IFD) is a diagram that shows how information is communicated (or "flows") from a source to a receiver or target (e.g. A→C), through some medium. [1]: 36–39 The medium acts as a bridge, a means of transmitting the information. Examples of media include word of mouth, radio, email, etc.