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--- melange:papers:spring2021 [2021/02/08 06:52]
lnarmour
+++ melange:papers:spring2021 [2021/02/08 08:10] (current)
lnarmour
@@ Line 1: / Line 1: @@
-@misc{li2021analytical,
+@article{10.5555/3322706.3322734,
-      title={Analytical Characterization and Design Space Exploration for Optimization of CNNs},
+author = {Bingham, Eli and Chen, Jonathan P. and Jankowiak, Martin and Obermeyer, Fritz and Pradhan, Neeraj and Karaletsos, Theofanis and Singh, Rohit and Szerlip, Paul and Horsfall, Paul and Goodman, Noah D.},
-      author={Rui Li and Yufan Xu and Aravind Sukumaran-Rajam and Atanas Rountev and P. Sadayappan},
+title = {Pyro: Deep Universal Probabilistic Programming},
-      year={2021},
+year = {2019},
-      eprint={2101.09808},
+issue_date = {January 2019},
-      archivePrefix={arXiv},
+publisher = {JMLR.org},
-      primaryClass={cs.LG},
+volume = {20},
-      url={https://arxiv.org/pdf/2101.09808.pdf}
+number = {JMLR},
+issn = {1532-4435},
+loc = {J. Mach. Learn. Res.},
+month = jan,
+pages = {973–978},
+numpages = {6},
+keywords = {probabilistic programming, deep learning, approximate Bayesian inference, graphical models, generative models},
+url = {https://paperswithcode.com/paper/pyro-deep-universal-probabilistic-programming}
 }
+@article{li2021analytical,
+title={Analytical Characterization and Design Space Exploration for Optimization of CNNs},
+author={Rui Li and Yufan Xu and Aravind Sukumaran-Rajam and Atanas Rountev and P. Sadayappan},
+year={2021},
+eprint={2101.09808},
+archivePrefix={arXiv},
+primaryClass={cs.LG},
+url={https://arxiv.org/pdf/2101.09808.pdf},
+loc={The ACM Conference on Architectural Support for Programming Languages and Operating
+Systems},
+number={ASPLOS}
+}
 @article{10.1145/3434321,
-author = {Courant, Nathana\"{e}l and Leroy, Xavier},
+author = {Courant, Nathanaël and Leroy, Xavier},
 title = {Verified Code Generation for the Polyhedral Model},
 year = {2021},
@@ Line 21: / Line 41: @@
 url = {https://doi.org/10.1145/3434321},
 doi = {10.1145/3434321},
-abstract = {The polyhedral model is a high-level intermediate representation for loop nests that supports elegantly a great many loop optimizations. In a compiler, after polyhedral loop optimizations have been performed, it is necessary and difficult to regenerate sequential or parallel loop nests before continuing compilation. This paper reports on the formalization and proof of semantic preservation of such a code generator that produces sequential code from a polyhedral representation. The formalization and proofs are mechanized using the Coq proof assistant.},
 journal = {Proc. ACM Program. Lang.},
 month = jan,
 articleno = {40},
 numpages = {24},
-keywords = {Polyhedral model, Polyhedral code generation, Compiler verification}
+keywords = {Polyhedral model, Polyhedral code generation, Compiler verification},
+loc = {Proc. ACM Program. Lang.}
 }
+@article{Ghahramani2015,
+author={Ghahramani, Zoubin},
+title={Probabilistic machine learning and artificial intelligence},
+journal={Nature},
+year={2015},
+month={May},
+day={01},
+number={521},
+pages={452-459},
+issn={1476-4687},
+doi={10.1038/nature14541},
+url={https://doi.org/10.1038/nature14541},
+loc={Nature}
+}
+@article{10.1145/3341702,
+author = {Walia, Rajan and Narayanan, Praveen and Carette, Jacques and Tobin-Hochstadt, Sam and Shan, Chung-chieh},
+title = {From High-Level Inference Algorithms to Efficient Code},
+year = {2019},
+issue_date = {August 2019},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+volume = {3},
+number = {ICFP},
+url = {https://doi.org/10.1145/3341702},
+doi = {10.1145/3341702},
+journal = {Proc. ACM Program. Lang.},
+month = jul,
+articleno = {98},
+numpages = {30},
+keywords = {loop optimization, conjugacy, marginalization, collapsed Gibbs sampling, arrays, plates, multidimensional distributions, probabilistic programs, map-reduce},
+loc = {Proc. ACM Program. Lang.}
+}

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