Search "+KenLM -Deep learning"

Positive matches

• 0.486 + - Natural language processing
• 0.088 + - NLP
• 0.077 + - Biomedical natural language processing
• 0.059 + - spaCy
• 0.052 + - CNN
• 0.031 + - StanfordNLP
• 0.028 + - scispaCy
• 0.026 + - Lexical feature selection
• 0.024 + - Convolutional neural network
• 0.007 + - Text analysis
• 0.007 + - RNN
• 0.006 + - Residual Learning
• 0.005 + - Computational creativity
• 0.004 + - Gradient descent
• 0.004 + - Recurrent neural network
• 0.001 + - Neural network
• 0.001 + - Data analysis
• 0.000 + - Pointwise mutual information

Negative matches

• 0.117 + - Language
• 0.043 + - Amazon EC2
• 0.034 + - Reinforcement learning
• 0.034 + - Residual leanrning
• 0.031 + - LSTM
• 0.026 + - Paper/Hochreiter1997
• 0.024 + - Attention
• 0.021 + - Evolution strategy
• 0.020 + - Amazon Web Services
• 0.014 + - Machine translation
• 0.014 + - Geoffrey Hinton
• 0.014 + - Learning
• 0.014 + - Deep learning and graphs
• 0.014 + - word2vec
• 0.014 + - GraphLab
• 0.014 + - Neural doodle
• 0.014 + - Keras
• 0.014 + - Meta learning
• 0.014 + - Amazon Elastic Compute Cloud
• 0.014 + - Yoshua Bengio