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CONNECTION
2004
98views more  CONNECTION 2004»
13 years 7 months ago
Self-refreshing memory in artificial neural networks: learning temporal sequences without catastrophic forgetting
While humans forget gradually, highly distributed connectionist networks forget catastrophically: newly learned information often completely erases previously learned information. ...
Bernard Ans, Stephane Rousset, Robert M. French, S...
CHI
2004
ACM
14 years 7 months ago
If not now, when?: the effects of interruption at different moments within task execution
User attention is a scarce resource, and users are susceptible to interruption overload. Systems do not reason about the effects of interrupting a user during a task sequence. In ...
Piotr D. Adamczyk, Brian P. Bailey
EDM
2008
97views Data Mining» more  EDM 2008»
13 years 8 months ago
Using Item-type Performance Covariance to Improve the Skill Model of an Existing Tutor
Using data from an existing pre-algebra computer-based tutor, we analyzed the covariance of item-types with the goal of describing a more effective way to assign skill labels to it...
Philip I. Pavlik, Hao Cen, Lili Wu, Kenneth R. Koe...
BVAI
2007
Springer
14 years 1 months ago
Classification with Positive and Negative Equivalence Constraints: Theory, Computation and Human Experiments
We tested the efficiency of category learning when participants are provided only with pairs of objects, known to belong either to the same class (Positive Equivalence Constraints ...
Rubi Hammer, Tomer Hertz, Shaul Hochstein, Daphna ...
HCI
2009
13 years 5 months ago
Seeing the World through an Expert's Eyes: Context-Aware Display as a Training Companion
Responsive Adaptive Display Anticipates Requests (RADAR) is a domain general system that learns to highlight an individual's preferred information displays, given the current ...
Marc T. Tomlinson, Michael Howe, Bradley C. Love