Search "+Peter Norvig +Automated machine learning +Class imbalance problem +Travelling salesman problem +Machine learning -Andrew Ng +Carlos Guestrin +Class imbalance +Poker +Gradient descent +Probability, Paradox, and the Reasonable Person Principle +Cluster analysis +Deep learning +Paradox -Neuron"

Positive matches

• 0.046 + - Probability
• 0.033 + - Simpson's paradox
• 0.024 + - Ulrike von Luxburg
• 0.024 + - Data clustering
• 0.017 + - Language
• 0.014 + - Jupyter/notebook
• 0.012 + - IPython notebook
• 0.012 + - Recommender system
• 0.009 + - Boosting (machine learning)
• 0.009 + - Residual leanrning
• 0.008 + - Artificial neural network
• 0.008 + - Reinforcement learning
• 0.008 + - GraphLab
• 0.007 + - Learning
• 0.007 + - Tom M. MItchell
• 0.007 + - Decision tree
• 0.007 + - Training dataset
• 0.007 + - Image completion
• 0.007 + - Privacy and Deanonymization
• 0.007 + - Sargur Srihari

Negative matches

• 0.033 + - Neural network
• 0.001 + - sense2vec
• 0.001 + - Clinical concept embedding
• 0.001 + - Paper/Perozzi2014
• 0.001 + - Gender bias
• 0.001 + - Chris McCormick
• 0.001 + - Paper/Levy2014a
• 0.001 + - Wolfgang Maass
• 0.000 + - Rami Al-Rfou
• 0.000 + - Paper/Bahdanau2015
• 0.000 + - Ivo Danihelka
• 0.000 + - KoNLPy
• 0.000 + - Paper
• 0.000 + - Topic modeling
• 0.000 + - Alex Graves
• 0.000 + - Jack Terwilliger
• 0.000 + - Neural Turing machine
• 0.000 + - Paper/Graves2014NTM
• 0.000 + - Paper/Hamilton2016
• 0.000 + - Liquid state machine