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Positive matches

• 0.033 + - Graph visualization
• 0.033 + - Linguistic diversity
• 0.020 + - Science policy
• 0.019 + - Probability theory
• 0.017 + - Public health policy
• 0.015 + - Pointwise mutual information
• 0.013 + - Text analysis
• 0.012 + - Data analysis
• 0.011 + - Probability, Paradox, and the Reasonable Person Principle
• 0.011 + - Probability distribution
• 0.010 + - Interdisciplinary research
• 0.009 + - tf-idf
• 0.008 + - Hessian matrix
• 0.007 + - Industry-Academic Partnership
• 0.006 + - CNN
• 0.006 + - Computer science
• 0.005 + - Machine learning
• 0.005 + - Cross entropy
• 0.004 + - NLP
• 0.004 + - David Feldman

Negative matches

• 0.010 + - Convolutional neural network
• 0.009 + - Deep learning
• 0.008 + - Residual leanrning
• 0.003 + - Beer
• 0.003 + - Artificial neural network
• 0.002 + - Meta learning
• 0.002 + - Learning
• 0.001 + - Autoencoder
• 0.001 + - Dropout
• 0.001 + - Yoshua Bengio
• 0.001 + - Amazon Elastic Compute Cloud
• 0.001 + - Generative adversarial networks
• 0.000 + - Geoffrey Hinton
• 0.000 + - Skip-gram
• 0.000 + - Yann LeCun
• 0.000 + - Brain and deep learning
• 0.000 + - TensorFlow
• 0.000 + - Generative adversarial network
• 0.000 + - Keras
• 0.000 + - Marc'Aurelio Ranzato