add urls

Author: bamos

publications/all.bib

@@ -186,6 +186,7 @@ @misc{press2025algotune
   year={2025},
   _venue={NeurIPS Datasets and Benchmarks Track},
   url={https://arxiv.org/abs/2507.15887},
+  codeurl={https://github.com/oripress/AlgoTune},
   abstract={
     Despite progress in language model (LM) capabilities, evaluations
     have thus far focused on models’ performance on
@@ -479,6 +480,7 @@ @misc{zheng2023semi
   author = {Zheng, Qinqing and Henaff, Mikael and Amos, Brandon and Grover, Aditya},
   year = {2023},
   url = {https://arxiv.org/abs/2210.06518},
+  codeurl = {https://github.com/facebookresearch/ssorl/},
   _venue={ICML},
   abstract={
 Natural agents can effectively learn from multiple data sources that
@@ -588,6 +590,7 @@ @misc{retchin2023koopman
   year = {2023},
   _venue={ICML Differentiable Almost Everything Workshop},
   url={https://openreview.net/pdf?id=3W7vPqWCeM},
+  codeurl={https://github.com/mhr/kcpo-icml},
   abstract={
     We introduce Koopman Constrained Policy Optimization (KCPO),
     combining implicitly differentiable model predictive
@@ -1498,7 +1501,8 @@ @misc{amos2016deepcompletion
   author={Brandon Amos},
   year={2016},
   url={https://bamos.github.io/2016/08/09/deep-completion/},
-  _venue={Blog Post},
+  codeurl={https://github.com/bamos/dcgan-completion.tensorflow},
+  _venue={Blog},
   abstract={
     Content-aware fill is a powerful tool designers and photographers use to fill in unwanted or missing parts of images. Image completion and inpainting are closely related technologies used to fill in missing or corrupted parts of images. There are many ways to do content-aware fill, image completion, and inpainting. In this blog post, I present Raymond Yeh and Chen Chen et al.'s paper "Semantic Image Inpainting with Perceptual and Contextual Losses," which was just posted on arXiv on July 26, 2016. This paper shows how to use deep learning for image completion with a DCGAN.
   },