Search "+Food -Dumpling -Wine pairing +Culinary informatics -Instacart +Deep learning -Paper/Hochreiter1997 -LSTM -Q-learning"

Positive matches

• 0.056 + - Flavor network
• 0.039 + - Social media
• 0.023 + - Odor
• 0.023 + - Restaurants
• 0.019 + - Calorie
• 0.018 + - Residual leanrning
• 0.018 + - Catching Fire: How Cooking Made Us Human
• 0.013 + - Stretching
• 0.012 + - Olfaction
• 0.008 + - Diet
• 0.008 + - Amazon EC2
• 0.008 + - Attention
• 0.007 + - Health
• 0.005 + - Evolution strategy
• 0.005 + - Amazon Web Services
• 0.005 + - Beer
• 0.004 + - Cecilia Mascolo
• 0.004 + - Cinder toffee
• 0.004 + - Archetype
• 0.003 + - Learning

Negative matches

• 0.164 + - Long short-term memory
• 0.107 + - Reinforcement learning
• 0.100 + - Public datasets
• 0.100 + - Food pairing
• 0.073 + - Food choice
• 0.057 + - Paper
• 0.053 + - Chinese cuisine
• 0.049 + - Network datasets
• 0.030 + - Data
• 0.017 + - Natural language processing
• 0.008 + - RNN
• 0.006 + - Computational creativity
• 0.005 + - Paper/Cooijmans2016
• 0.004 + - Network science
• 0.003 + - Keras
• 0.003 + - Nicolas Ballas
• 0.002 + - Tomas Mikolov
• 0.002 + - Covariate shift
• 0.002 + - Tim Cooijmans
• 0.002 + - Taste