Search "+Diversity -Paper +Paper/Iacopini2018 +Paper/McNerney2018production +Paper/Graves2016DNC -Input–output model +Wassily Leontief -Network +Charles Savoie"

Positive matches

• 0.148 + - Innovation
• 0.065 + - Basic research
• 0.041 + - Future of work
• 0.037 + - Heaps' law
• 0.035 + - Innovation hot spots
• 0.034 + - Deep learning
• 0.032 + - Technological unemployment
• 0.027 + - Eugene Garfield
• 0.022 + - Neural Turing machine
• 0.022 + - Alex Graves
• 0.022 + - DeepMind
• 0.022 + - Differentiable neural computers
• 0.016 + - Economic network
• 0.015 + - James McNerney
• 0.015 + - Francesco Caravelli
• 0.014 + - Doyne Farmer
• 0.010 + - Meta learning
• 0.009 + - Natural language processing
• 0.007 + - Artificial neural network
• 0.005 + - Residual Learning

Negative matches

• 0.014 + - Network science
• 0.002 + - Network datasets
• 0.002 + - Paper/Tshitoyan2019unsupervised
• 0.002 + - Paper/Vincent2010
• 0.002 + - Paper/King2000makingthemost
• 0.002 + - Paper/Kloumann2016blockmodels
• 0.002 + - Paper/Gopalan2012community
• 0.002 + - Paper/Rodriguez2019optimal
• 0.002 + - Paper/MacKay1991
• 0.002 + - Paper/Hamilton2016
• 0.002 + - Paper/Levy2014a
• 0.002 + - Paper/Uzzi2013
• 0.002 + - Paper/Rajtmajer2012
• 0.002 + - Paper/Airoldi2008mmsbm
• 0.002 + - Paper/Chiquet2018
• 0.002 + - Paper/Cheng2015
• 0.002 + - Paper/Heaberlin2015
• 0.002 + - Paper/Kim2015
• 0.002 + - Paper/Perozzi2014
• 0.002 + - Paper/Youn2015