Cengiz Pehlevan
Harvard University
Assistant Professor
Applied Mathematics (SEAS)
Program in Neuroscience (DMS) (HILS)
Research Area
#Computer science#Artificial neural network#Artificial intelligence#Hebbian theory#Pattern recognition#Algorithm#Principal component analysis#Dimensionality reduction#Mathematics#Sensory system
SCIE paper information
Papers relevant to ‘Artificial neural network’: 7
Research performance of SCIE papers matching with ‘Artificial neural network’
*Papers published between 2014 and 2020 were selected, and the citation number was determined by bing.com.
A hebbian/anti-hebbian neural network for linear subspace learning: A derivation from multidimensional scaling of streaming data
2015/07 NEURAL COMPUTATION 1.626 Impact Factor 67 citations
Why Do Similarity Matching Objectives Lead to Hebbian/Anti-Hebbian Networks?
2018/01 NEURAL COMPUTATION 2.261 Impact Factor 36 citations
Papers for author ‘Cengiz Pehlevan’: 12
Number of published SCIE papers by year
*Papers published between 2014 and 2020 were selected, and the citation number was determined by bing.com.
Acute off-target effects of neural circuit manipulations
2015/12 NATURE 38.602 Impact Factor 255 citations
A hebbian/anti-hebbian neural network for linear subspace learning: A derivation from multidimensional scaling of streaming data
2015/07 NEURAL COMPUTATION 1.626 Impact Factor 67 citations
conference information
Papers relevant to ‘Artificial neural network’: 5
Research performance of Top-tier Conference matching with ‘Artificial neural network’
*Papers published between 2014 and 2020 were selected, and the citation number was determined by bing.com.
A normative theory of adaptive dimensionality reduction in neural networks
2015/12 NEURIPS Top-tier Conference 24 citations
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks
2018/01 NEURIPS Top-tier Conference 20 citations
Papers for author ‘Cengiz Pehlevan’: 6
Number of published Top-tier Conference by year
*Papers published between 2014 and 2020 were selected, and the citation number was determined by bing.com.
A normative theory of adaptive dimensionality reduction in neural networks
2015/12 NEURIPS Top-tier Conference 24 citations
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks
2018/01 NEURIPS Top-tier Conference 20 citations
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