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Positive matches

• 0.060 + - Graph embedding
• 0.027 + - Science policy
• 0.017 + - Opioid crisis
• 0.017 + - Harold Lasswell
• 0.017 + - Industry-Academic Partnership
• 0.017 + - Paper/Nielsen2017
• 0.016 + - Sentence embedding
• 0.011 + - Paper/MacKay1991
• 0.011 + - word2vec
• 0.011 + - GloVe
• 0.007 + - Topic model
• 0.007 + - Embedded topic model
• 0.006 + - CNN
• 0.005 + - Edwin Thompson Jaynes
• 0.005 + - Peer review
• 0.005 + - Paper/Tshitoyan2019unsupervised
• 0.005 + - Software engineering
• 0.005 + - Bibliometrics
• 0.005 + - Author name disambiguation
• 0.005 + - Probability

Negative matches

• 0.025 + - Lexical feature selection
• 0.004 + - Kullback–Leibler divergence
• 0.004 + - Cross entropy
• 0.004 + - Gibbs' inequality
• 0.004 + - David Feldman
• 0.004 + - Information Theory, Inference, and Learning Algorithms
• 0.003 + - Machine learning
• 0.001 + - Pointwise mutual information
• 0.001 + - Class imbalance problem
• 0.001 + - Class imbalance
• 0.001 + - Automated machine learning
• 0.000 + - Future of work
• 0.000 + - scikit-learn
• 0.000 + - Recommender system
• 0.000 + - Gradient descent
• 0.000 + - Regular expression
• 0.000 + - Social network
• 0.000 + - Pathogen transmission
• 0.000 + - PyTorch
• 0.000 + - Python