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Positive matches

• 0.059 + - Graph embedding
• 0.043 + - Research advices
• 0.031 + - Science policy
• 0.017 + - Public health policy
• 0.017 + - Citation
• 0.011 + - Literary allusions in science
• 0.011 + - Paper/MacKay1991
• 0.011 + - Information theory
• 0.010 + - CNN
• 0.009 + - Continuous embedding
• 0.007 + - NLP
• 0.007 + - Jeannette M. Wing
• 0.006 + - Scientific communication
• 0.006 + - Aaron Clauset
• 0.006 + - Collaborative software
• 0.006 + - GloVe
• 0.006 + - Larry Wasserman
• 0.006 + - Paper/Tshitoyan2019unsupervised
• 0.006 + - Remix
• 0.006 + - word2vec

Negative matches

• 0.010 + - Deep learning
• 0.009 + - Residual leanrning
• 0.007 + - Convolutional neural network
• 0.003 + - Beer
• 0.002 + - Artificial neural network
• 0.002 + - Meta learning
• 0.002 + - Learning
• 0.001 + - Jürgen Schmidhuber
• 0.001 + - Marc'Aurelio Ranzato
• 0.001 + - Andrew Ng
• 0.001 + - Yann LeCun
• 0.001 + - Brain and deep learning
• 0.001 + - Autoencoder
• 0.001 + - Generative adversarial networks
• 0.001 + - Generative adversarial network
• 0.001 + - Yoshua Bengio
• 0.001 + - Keras
• 0.001 + - Language
• 0.001 + - Machine translation
• 0.001 + - Deep learning and graphs