Updated README.

Author: otiliastr

research/gam/README.md

@@ -37,20 +37,41 @@ versions (such as 2.0 or above) or Python versions.
 ## How to run
 
 To run GAM on a graph-based dataset (e.g., Cora, Citeseer, Pubmed), from this
-folder run: `bash python3.7 -m gam.experiments.run_train_gam_graph
---data_path=<path_to_data>`
+folder run: 
+```
+$ python3.7 -m gam.experiments.run_train_gam_graph --data_path=<path_to_data>
+```
 
 To run GAM on datasets without a graph (e.g., CIFAR10), from this folder run:
-`bash python3.7 -m gam.experiments.run_train_gam`
+```
+$ python3.7 -m gam.experiments.run_train_gam
+```
+
+We recommend running on a GPU. With CUDA, this can be done by prepending
+`CUDA_VISIBLE_DEVICES=<your-gpu-number>` in front of the run script.
 
 For running on different datasets and configuration, please check the command
 line flags in each of the run scripts.
 
+
+## Visualizing the results.
+
+To visualize the results in Tensorboard, use the following command, adjusting
+the dataset name accordingly:
+```
+$ tensorboard --logdir=outputs/summaries/cora
+```
+
+An example of such visualization for Cora with GCN + GAM model is the following, 
+showing the accuracy per co-train iteration for 3 runs with 3 different random seeds:
+![Tensorboard plot](gam_gcn_cora_multiple_seeds.png?raw=true "GCN + GAM on Cora")
+
+
 ## References
 
-[[1] O. Stretcu, K. Viswanathan, D. Movshovitz-Attias, E.A. Platanios, A.
-Tomkins, S. Ravi. "Graph Agreement Models for Semi-Supervised Learning." NeurIPS
-2019](https://nips.cc/Conferences/2019/Schedule?showEvent=13925)
+[[1] O. Stretcu, K. Viswanathan, D. Movshovitz-Attias, E.A. Platanios, S. Ravi,
+A. Tomkins. "Graph Agreement Models for Semi-Supervised Learning." NeurIPS
+2019](https://papers.nips.cc/paper/9076-graph-agreement-models-for-semi-supervised-learning)
 
 [[2] T. Bui, S. Ravi and V. Ramavajjala. "Neural Graph Learning: Training Neural
 Networks Using Graphs." WSDM 2018](https://ai.google/research/pubs/pub46568.pdf)