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Related Concept Videos

Transformers in Distribution System01:27

Transformers in Distribution System

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
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Types Of Transformers01:16

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
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Transformers01:26

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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Storage01:23

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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Energy Losses in Transformers01:21

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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Shared sensitivity to data distribution during learning in humans and transformer networks.

Jacques Pesnot Lerousseau1,2,3, Christopher Summerfield4

  • 1Department of Experimental Psychology, University of Oxford, Oxford, UK. jacques.pesnot-lerousseau@univ-amu.fr.

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Summary

Humans and transformer networks learn similarly by balancing example diversity and redundancy. However, only humans benefit from dynamic training schedules emphasizing diverse examples early in learning.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Understanding human learning mechanisms is crucial for advancing AI.
  • Transformer networks are state-of-the-art deep learning models.
  • Comparing human and AI learning can reveal fundamental principles.

Purpose of the Study:

  • To investigate whether humans learn similarly to transformer networks.
  • To compare 'in-context' and 'in-weights' learning in both humans and AI.
  • To examine the impact of training data distribution on learning strategies.

Main Methods:

  • Trained 530 humans and transformer networks on a rule learning task.
  • Manipulated training data diversity and redundancy.
  • Measured generalization to novel queries ('in-context' learning) and memory recall ('in-weights' learning).

Main Results:

  • Humans and transformers showed similar responses to training data manipulations.
  • Redundancy and diversity traded off in driving 'in-weights' and 'in-context' learning, respectively.
  • A balanced mix of diversity and redundancy enabled tandem learning strategies in both.

Conclusions:

  • Data-distributional properties impacting learning are conserved across humans and transformers.
  • Humans, unlike transformers, benefit from dynamic training curricula emphasizing early diversity.
  • While core learning mechanisms show parallels, human learning exhibits unique curriculum sensitivity.