Added references to notebook

Tutorials/CNTK_206_Training_with_Sampled_Softmax.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Sampled Softmax\n",
     "\n",
-    "For classification and prediction problems a typical criterion function is cross-entropy with softmax. If the number of output classes is high the computation of this criterion and the corresponding gradients could be quite costly. Sampled Softmax is a heuristic to speed up training in these cases.\n",
+    "For classification and prediction problems a typical criterion function is cross-entropy with softmax. If the number of output classes is high the computation of this criterion and the corresponding gradients could be quite costly. Sampled Softmax is a heuristic to speed up training in these cases. (see: [Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model](http://www.iro.umontreal.ca/~lisa/pointeurs/importance_samplingIEEEtnn.pdf), [Exploring the Limits of Language Modeling](https://arxiv.org/pdf/1602.02410v1.pdf), [What is Candidate Sampling](https://www.tensorflow.org/extras/candidate_sampling.pdf))\n",
     "\n",
     "## Basics\n",
     "\n",