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Understanding Failures of Learning:Hebbian Learning,Competition for Representational Space,and some Preliminary Experimental Data
Understanding Failures of Learning Hebbian Learning Competition for Representational Space Experimental Data
2015/6/19
The availability of powerful learning algorithms such as back-propagation has created a situation in which we now know how to teach neural networks many complex things. Models that use back propagatio...
Role of the hippocampus in learning and memory:A computational analysis
hippocampus learning memory computational analysis
2015/6/19
Role of the hippocampus in learning and memory:A computational analysis.
Role of the Hippocampus In Learning and Memory:A Computational Analysis
Hippocampus Learning Memory Computational Analysis
2015/6/19
Role of the Hippocampus In Learning and Memory:A Computational Analysis.
Considerations Arising From a Complementary Learning Systems Perspective on Hippocampus and Neocortex
consolidation amnesia memory interleaved learning
2015/6/19
We discuss a framework for the organization of learning systems in the mammalian brain, in which the hippocampus and related areas form a memory system complementary to learning mechanisms in neocorte...
Why There Are Complementary LearningSystems in the Hippocampus and Neocortex:InsightsFrom the Successesand Failuresof Connectionist Modelsof Learning andMemory
Complementary Learning Systems Hippocampus and Neocortex Successes And Failures Connectionist Models Learning and Memory
2015/6/19
Damage to the hippocampal system disrupts recent memory but leaves remote memory intact. The account presented here suggests that memories are first stored via synaptic changes in the hippocampal syst...
Learning the general but not the specific.
Learning continuous probability distributions with symmetric diffusion networks
continuous probability distributions symmetric diffusion networks
2015/6/19
Learning continuous probability distributions with symmetric diffusion networks.
How is complex sequential material acquired, processed, and represented when there is no intention to learn?Two experiments exploringa choicereaction time task are reported. Unknown to Ss, successives...
Learning and applying contextual constraints in sentence comprehension
Learning applying contextual sentence comprehension
2015/6/19
Learning and applying contextual constraints in sentence comprehension.
Learning representations by recirculation.
Scholars of language and psycholinguistics have been among the first to stress the importance of rules in describing human behavior. The reason for this is obvious. Many aspects of language can be cha...
AAVE/Creole copula absence:A critique of the imperfect learning hypothesis
AAVE/Creole copula absence imperfect learning hypothesis
2015/6/16
This study confirms the robustness of the finding in the literature on African American Vernacular English [AAVE] and creole English (especially in the Caribbean) that omission of copular and auxiliar...
“Was it good? It was provocative.” Learning the meaning of scalar adjectives
Was it good? It was provocative scalar adjectives
2015/6/15
Texts and dialogues often express information indirectly. For instance, speakers’ answers to yes/no questions do not always straightforwardly convey a ‘yes’ or ‘no’ answer. The intended reply is clear...
Unsupervised vector-based approaches to semantics can model rich lexical meanings, but they largely fail to capture sentiment information that is central to many word meanings and important for a wide...
Spectral Learning
Spectral Learning
2015/6/12
We present a simple, easily implemented spectral learning algorithm that applies equally whether we have no supervisory information, pairwise link constraints, or labeled examples. In the unsupervised...