title: Conv1D with kernel_size=1 vs Linear layer
body: <p>I'm working on very sparse vectors as input. I started working with simple <code>Linear</code> (dense/fully connected layers) and my network yielded pretty good results (let's take accuracy as my metric here, 95.8%).</p>
  <p>I later tried to use a <code>Conv1d</code> with a <code>kernel_size=1</code> and a <code>MaxPool1d</code>, and this network works slightly better (96.4% accuracy).</p>
  <p><em>Question</em>: How are these two implementation different ? Shouldn't a <code>Conv1d</code> with a unit <code>kernel_size</code> do the same as a <code>Linear</code> layer?</p>
  <p>I've tried multiple runs, the CNN always yields slightly better results.</p>