Search "+Food -Beer -Differentiable neural computers -Rice -Food pairing -Molecular gastronomy -Dim sum +Deep learning +Evolution strategy"

Positive matches

• 0.114 + - Reinforcement learning
• 0.040 + - Social media
• 0.026 + - Q-learning
• 0.022 + - Odor
• 0.020 + - Residual leanrning
• 0.010 + - Modeling animal behaviors with reinforcement learning
• 0.010 + - Stretching
• 0.009 + - Amazon EC2
• 0.007 + - Music
• 0.006 + - Attention
• 0.006 + - Paper/Hochreiter1997
• 0.005 + - LSTM
• 0.004 + - Amazon Web Services
• 0.004 + - Recurrent neural network
• 0.004 + - Deep generative models
• 0.004 + - Autoencoder
• 0.004 + - Yann LeCun
• 0.003 + - Yoshua Bengio
• 0.003 + - Cecilia Mascolo
• 0.003 + - Long short-term memory

Negative matches

• 0.110 + - Cooking
• 0.055 + - Chinese cuisine
• 0.055 + - Paper/Graves2016DNC
• 0.028 + - Trappist beer
• 0.026 + - Andy Connelly
• 0.022 + - Bread
• 0.022 + - Cinder toffee
• 0.021 + - Cheese
• 0.016 + - Catching Fire: How Cooking Made Us Human
• 0.015 + - Taste
• 0.015 + - Flavor
• 0.014 + - Wender Bredie
• 0.013 + - Meta learning
• 0.013 + - Food choice
• 0.013 + - Food science
• 0.009 + - Restaurants
• 0.008 + - Calorie
• 0.008 + - Health
• 0.007 + - Olfaction
• 0.006 + - Artificial neural network