title: What is the advantage of not having pooling layers in between convolutions in this context?
body: <p>In the context of a convolutional neural network designed to extract DNA motifs, why would one stack convolution layers without max pooling functions in between?</p>
  
  <p>Here's the context in which this architecture appears.</p>
  
  <pre><code>self.model = Sequential()
  assert len(num_filters) == len(conv_width)
  for i, (nb_filter, nb_col) in enumerate(zip(num_filters, conv_width)):
      conv_height = 4 if i == 0 else 1
      self.model.add(Convolution2D(
          nb_filter=nb_filter, nb_row=conv_height,
          nb_col=nb_col, activation='linear',
          init='he_normal', input_shape=self.input_shape,
          W_regularizer=l1(L1), b_regularizer=l1(L1)))
      self.model.add(Activation('relu'))
      self.model.add(Dropout(dropout))
  self.model.add(MaxPooling2D(pool_size=(1, pool_width)))
  </code></pre>