diff --git a/README.md b/README.md
index 048d9ce..ef5af73 100644
--- a/README.md
+++ b/README.md
@@ -42,79 +42,142 @@ NNSVD and NMF
 
 # End-to-End Code Execution Guide
 
-## **1. Training Models**
+## 1. Training Models
 
 First, set the environment variables specific to your experiment in `send_train_jobs.sh`.
 
-### **Output:**
+### Output:
 
 - Models will be stored in `my_checkpoints`. For example: `my_checkpoints/cifar10/resnet18_0_1.ckpt`
 - If using CSVLogger, log data will be saved in `log_{dataset_name}_{model_name}_test/{fold_ind}_{nfolds}`
 
-### **To Run:**
+### To Run:
 
-\`\`\`bash
+```bash
 bash send_train_jobs.sh
-\`\`\`
+```
 
-## **2. Generating Attack Data**
+## 2. Generating Attack Data
 
 Set the environment variables for the experiment in `send_attack_jobs.sh`.
 
-### **Output:**
+### Output:
 
 - Attack data and logs will be stored in `attack_log_{dataset_name}/{model_name}/{fold_ind}_{nfolds}_{attack_type}`
 
-### **To Run:**
+### To Run:
 
-\`\`\`bash
+```bash
 bash send_attack_jobs.sh
-\`\`\`
+```
 
-## **3. Parameter Tuning**
+## 3. Parameter Tuning
 
 Set the environment variables for the experiment in `send_tune_jobs.sh`.
 
-### **Output:**
+### Output:
 
 - The tuning results will be located at `parameter_search_{dataset_name}/{model_name}/{fold_ind}_{nfolds}_{attack_type}/search_results`
 
-### **To Run:**
+### To Run:
 
-\`\`\`bash
+```bash
 bash send_tune_jobs.sh
-\`\`\`
+```
 
-## **4. Collecting Results**
+## 4. Collecting Results
 
 After setting the environment variables:
 
-### **Output:**
+### Output:
 
 - The results will be stored as CSV files in `tune_csv_results`
 
-### **To Run:**
+### To Run:
 
-\`\`\`bash
+```bash
 bash collect_results.sh
-\`\`\`
+```
 
-## **5. Final Evaluation on Test Set**
+## 5. Final Evaluation on Test Set
 
-### **Generating Attacks**
+### Generating Attacks
 
 - Set `final_test_set` to `True` in `send_attack_jobs.sh`
 
-\`\`\`bash
+```bash
 bash send_attack_jobs.sh
-\`\`\`
+```
 
-### **Evaluating Models**
+### Evaluating Models
 
 - After setting specific environment variables (such as `eval_top_k`):
 
-### **To Run:**
+### To Run:
 
-\`\`\`bash
+```bash
 bash send_final_eval_jobs.sh
-\`\`\`
+```
+
+## Authors
+
+* [Manish Bhattarai](mailto:ceodspspectrum@lanl.gov) - Los Alamos National Laboratory
+* [Mehmet Kaymak Cagri](mailto:mehmet@lanl.gov) - Los Alamos National Laboratory
+* [Ben Nebgen](mailto:bnebgen@lanl.gov) - Los Alamos National Laboratory
+* [Boian Alexandrov](mailto:boian@lanl.gov) - Los Alamos National Laboratory
+* [Kim Rasmussen](mailto:kor@lanl.gov) - Theoretical Division, Los Alamos National Laboratory
+
+## How to cite pyDNMFk?
+
+```latex
+  @article{bhattarai2023robust,
+  title={Robust Adversarial Defense by Tensor Factorization},
+  author={Bhattarai, Manish and Kaymak, Mehmet Cagri and Barron, Ryan and Nebgen, Ben and Rasmussen, Kim and Alexandrov, Boian},
+  journal={arXiv preprint arXiv:2309.01077},
+  year={2023}
+}
+```
+
+
+## Acknowledgments
+Los Alamos National Lab (LANL), T-1
+
+## Copyright Notice
+© (or copyright) 2023. Triad National Security, LLC. All rights reserved.
+This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
+National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
+Department of Energy/National Nuclear Security Administration. All rights in the program are
+reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear
+Security Administration. The Government is granted for itself and others acting on its behalf a
+nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare
+derivative works, distribute copies to the public, perform publicly and display publicly, and to permit
+others to do so.
+
+
+## License
+
+This program is open source under the BSD-3 License.
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.