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...n expressive output space while maintaining modularity and tractability of training and inference. ...n expressive output space while maintaining modularity and tractability of training and inference. These constraints can express either hard restrictions, comp ...

...<ref name=":0" /> It has applications in flight training,<ref name=":1" /> cognitive assessment,<ref name=":1" /> disease diagnosis,<ref name=":2" /> and treatm ...e utilized oculometers in their training programs, creating the Oculometer Training Tape Technique in the late 1900s.<ref name=":1" /> ...

...ref> In professional learning contexts, individuals may "test out" of some training to ensure they engage with novel instruction. Computers adapt the presentat ...y application is in education, but another popular application is business training. They have been designed as desktop computer applications, web application ...

...led information about a student’s cognitive strengths and weaknesses. This cognitive diagnostic feedback has the potential to guide instructors, parents and stu ...utes are represented using a hierarchical structure so the ordering of the cognitive skills is specified. This model provides a framework for designing diagnost ...

...inator provides a better learning signal to the generator. This allows the training to be more stable when generator is learning distributions in very high dim ...Cseke |first2=Botond |last3=Tomioka |first3=Ryota |date=2016 |title=f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization |url=h ...

...domains including [[embodied cognition]], [[developmental psychology]], [[cognitive neuroscience]], and [[social psychology]]. ...7 | title = Perception and action planning | journal = European Journal of Cognitive Psychology | volume = 9 | issue = 2| pages = 129–154 | doi=10.1080/71375255 ...

Error-driven learning has widespread applications in [[Cognitive science|cognitive sciences]] and [[computer vision]]. These methods have also found successfu === Cognitive science === ...

=== Example: Modeling cognitive decline in Alzheimer's disease === ...s that are each categorized as having either normal cognition (CN), [[mild cognitive impairment]] (MCI) or [[dementia]] (DEM) at the baseline visit (time <math> ...

...each time we take a set of [[sample (statistics)|samples]] to create a new training data set. It is said that there is greater [[variance]] in the model's [[es ...revent [[supervised learning]] algorithms from generalizing beyond their [[training set]]:<ref name=":0">{{cite journal |last1=Kohavi |first1=Ron |last2=Wolper ...

|hdl-access=free}}</ref> [[Interval training|Sprint interval testing]] such as is similar to the construction of the Win |title=10 or 30-s sprint interval training bouts enhance both aerobic and anaerobic performance ...

...=http://www.ntu.edu.sg/home/egbhuang/pdf/ELM-Randomness-Kernel.pdf|journal=Cognitive Computation|volume=6|issue=3|pages=376–390|doi=10.1007/s12559-014-9255-2|s2 ...=http://www.ntu.edu.sg/home/egbhuang/pdf/ELM-Randomness-Kernel.pdf|journal=Cognitive Computation|volume=6|issue=3|pages=376–390|doi=10.1007/s12559-014-9255-2|s2 ...

...the pursuit of different stimuli in consecutive trials, despite a lack of training or reinforcement.<ref>{{cite journal |last1=d'Isa |first1=R. |last2=Comi |f ...in studying the potential neuroanatomical and neurobiological mediators of cognitive function,<ref name=":2">Richman, C. L., Dember, W. N., & Kim, P. (1986). Sp ...

...in the Perception of Biological Motion|author=Grossman|journal=Journal of Cognitive Neuroscience|year=2000|volume=12|pages=711–720|doi=10.1162/089892900562417| ===Cognitive Model of Biological Motion Form (Lange & Lappe, 2006)=== ...

...ly diverse teams “consist of individuals with a variety of educational and training backgrounds working together."<ref>Jackson, S.E. (1996). The consequences o ..."European Journal of Social Psychology,13(1), 45-55.</ref> In dealing with cognitive or creative tasks, functional diversity proved to helpful.<ref>Guzzo, R.A. ...

...oblem, which tries to ''distinguish between'' two or more classes with the training set containing objects from all the classes. Examples include the monitorin ...ent Trends in One Class Classification|journal=Artificial Intelligence and Cognitive Science|volume=6206|series=Lecture Notes in Computer Science|language=en|pu ...

...Moser EI, Moser MB | title = Path integration and the neural basis of the "cognitive map". | journal = Nat. Rev. Neurosci. | volume = 7 | issue = 8 | pages = 66 ...rious attractors and poorly conditioned basins of attraction. Furthermore, training on attractor networks is generally computationally expensive, compared to o ...

...opposite case it is cleared. Note that the thresholds may be zero, if the training input vectors are closed to orthogonal ones. ...es from a training set when this approximation is valid, that is, when the training set contains sufficient data to provide good estimates of the underlying [[ ...

...visual programming can have significant positive effects on performance in cognitive tasks.<ref>{{Cite book|chapter=Metacognitive theories of visual programming ...ce of functional evaluations over the weights from previous layers. During training, the machine learning algorithm uses optimization to minimize a loss functi ...

Unlike "hard" weights, which are computed during the backwards training pass, "soft" weights exist only in the forward pass and therefore change wi ...|Selective attention]] in humans had been well studied in neuroscience and cognitive psychology.<ref>{{Cite book |last1=Kramer |first1=Arthur F. |url=http://www ...

...ases with memory load in an attractor network model | journal = Journal of Cognitive Neuroscience | volume = 23 | issue = 10 | pages = 3008–20 | date = October ...on and co-activation probabilities <math>P_*</math> are estimated from the training set, which can be done e.g. by exponentially weighted moving averages (see ...