New method for compressing neural networks better preserves accuracy
Neural networks have been responsible for most of the top-performing AI systems of the past decade, but they tend to be big, which means they tend to be slow. That’s a problem for systems like Alexa, which depend on neural networks to process spoken requests in real time. In natural-language-understanding (NLU) applications, most of a neural network’s size comes from a huge lookup table that corre
From Structured Prediction to Inverse Reinforcement Learning
SPIRL @ AAAI 2011 1 Hal Daumé III (me@hal3.name) ���������������� ��� �� ��� �������� � ����� ������ � ���������� �������������� ����������� ���������� ������� ���� !�"�� ��������!!! �#$%% ����&������� '� ���� ���� � ����'�(�!�)����#$%% ��� �������� �� � ����� ���� ����������� � � � ��� � ����� ����� ���� ��� �� ��� ����� � ����� ����������� ��������� ������ �� ���������� ������!" ����##$%&$� ��"'
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Transferring Naive Bayes Classifiers for Text Classification
