var bibbase_data = {"data":"\"Loading..\"\n\n
\n\n \n\n \n\n \n \n\n \n\n \n \n\n \n\n \n
\n generated by\n \n \"bibbase.org\"\n\n \n
\n \n\n
\n\n \n\n\n
\n\n Excellent! Next you can\n create a new website with this list, or\n embed it in an existing web page by copying & pasting\n any of the following snippets.\n\n
\n JavaScript\n (easiest)\n
\n \n <script src=\"https://bibbase.org/show?bib=cs.mcgill.ca%2F%7Esiamak%2Fall.bib&jsonp=1&authorFirst=1&group0=year&group1=order&theme=mila&fullnames=1&hidemenu=1&jsonp=1\"></script>\n \n
\n\n PHP\n
\n \n <?php\n $contents = file_get_contents(\"https://bibbase.org/show?bib=cs.mcgill.ca%2F%7Esiamak%2Fall.bib&jsonp=1&authorFirst=1&group0=year&group1=order&theme=mila&fullnames=1&hidemenu=1\");\n print_r($contents);\n ?>\n \n
\n\n iFrame\n (not recommended)\n
\n \n <iframe src=\"https://bibbase.org/show?bib=cs.mcgill.ca%2F%7Esiamak%2Fall.bib&jsonp=1&authorFirst=1&group0=year&group1=order&theme=mila&fullnames=1&hidemenu=1\"></iframe>\n \n
\n\n

\n For more details see the documention.\n

\n
\n
\n\n
\n\n This is a preview! To use this list on your own web site\n or create a new web site from it,\n create a free account. The file will be added\n and you will be able to edit it in the File Manager.\n We will show you instructions once you've created your account.\n
\n\n
\n\n

To the site owner:

\n\n

Action required! Mendeley is changing its\n API. In order to keep using Mendeley with BibBase past April\n 14th, you need to:\n

    \n
  1. renew the authorization for BibBase on Mendeley, and
    2. \n
  2. update the BibBase URL\n in your page the same way you did when you initially set up\n this page.\n
    3. \n
\n

\n\n

\n \n \n Fix it now\n

\n
\n\n
\n\n\n
\n \n \n
\n
\n  \n 2020\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n  \n undefined\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n Siamak Ravanbakhsh.\n\n\n \n \n \n \n \n Universal Equivariant Multilayer Perceptrons.\n \n \n \n \n\n\n \n\n\n\n arXiv preprint arXiv:2002.02912. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"Universal arxiv\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 172 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{ravanbakhsh2020,\n  title={Universal Equivariant Multilayer Perceptrons},\n  author={Ravanbakhsh, Siamak},\n  journal={arXiv preprint arXiv:2002.02912},\n  url_arxiv = {https://arxiv.org/abs/2002.02912},\n  year={2020}\n}\n\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 2019\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n  \n undefined\n \n \n (5)\n \n \n
\n
\n \n \n
\n \n\n \n \n Marjan Albooyeh; Daniele Bertolini; and Siamak Ravanbakhsh.\n\n\n \n \n \n \n \n Incidence Networks for Geometric Deep Learning.\n \n \n \n \n\n\n \n\n\n\n arXiv preprint arXiv:1905.11460. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"Incidence arxiv\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{albooyeh2019incidence,\n  title={Incidence Networks for Geometric Deep Learning},\n  author={Albooyeh, Marjan and Bertolini, Daniele and Ravanbakhsh, Siamak},\n  journal={arXiv preprint arXiv:1905.11460},\n  url_arxiv = {https://arxiv.org/abs/1905.11460},\n  year={2019}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Siyu He; Yin Li; Yu Feng; Shirley Ho; Siamak Ravanbakhsh; Wei Chen; and Barnabas Poczos.\n\n\n \n \n \n \n Learning to predict the cosmological structure formation.\n \n \n \n\n\n \n\n\n\n Proceedings of the National Academy of Sciences,201821458. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{he2019learning,\n  title={Learning to predict the cosmological structure formation},\n  author={He, Siyu and Li, Yin and Feng, Yu and Ho, Shirley and Ravanbakhsh, Siamak and Chen, Wei and Poczos, Barnabas},\n  journal={Proceedings of the National Academy of Sciences},\n  pages={201821458},\n  year={2019},\n  publisher={National Acad Sciences}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Bahare Fatemi; Siamak Ravanbakhsh; and David Poole.\n\n\n \n \n \n \n Improved knowledge graph embedding using background taxonomic information.\n \n \n \n\n\n \n\n\n\n In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 3526–3533, 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{fatemi2019improved,\n  title={Improved knowledge graph embedding using background taxonomic information},\n  author={Fatemi, Bahare and Ravanbakhsh, Siamak and Poole, David},\n  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},\n  volume={33},\n  pages={3526--3533},\n  year={2019}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n KA Venn; S Fabbro; A Liu; Y Hezaveh; L Levasseur; G Eadie; S Ellison; J Woo; JJ Kavelaars; KM Yi; and others.\n\n\n \n \n \n \n LRP2020: Machine Learning Advantages in Canadian Astrophysics.\n \n \n \n\n\n \n\n\n\n arXiv preprint arXiv:1910.00774. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{venn2019lrp2020,\n  title={LRP2020: Machine Learning Advantages in Canadian Astrophysics},\n  author={Venn, KA and Fabbro, S and Liu, A and Hezaveh, Y and Levasseur, L and Eadie, G and Ellison, S and Woo, J and Kavelaars, JJ and Yi, KM and others},\n  journal={arXiv preprint arXiv:1910.00774},\n  year={2019}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Devon Graham; and Siamak Ravanbakhsh.\n\n\n \n \n \n \n \n Equivariant Entity-Relationship Networks.\n \n \n \n \n\n\n \n\n\n\n arXiv preprint arXiv:1903.09033. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"Equivariant arxiv\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{graham2019deep,\n  title={Equivariant Entity-Relationship Networks},\n  author={Graham, Devon and Ravanbakhsh, Siamak},\n  journal={arXiv preprint arXiv:1903.09033},\n  url_arxiv = {https://arxiv.org/abs/1903.09033},\n  year={2019}\n}\n\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 2018\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n  \n undefined\n \n \n (5)\n \n \n
\n
\n \n \n
\n \n\n \n \n Jakub M Tomczak; Szymon Zaręba; Siamak Ravanbakhsh; and Russell Greiner.\n\n\n \n \n \n \n Low-Dimensional Perturb-and-MAP Approach for Learning Restricted Boltzmann Machines.\n \n \n \n\n\n \n\n\n\n Neural Processing Letters,1–19. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{tomczak2018low,\n  title={Low-Dimensional Perturb-and-MAP Approach for Learning Restricted Boltzmann Machines},\n  author={Tomczak, Jakub M and Zar{\\k{e}}ba, Szymon and Ravanbakhsh, Siamak and Greiner, Russell},\n  journal={Neural Processing Letters},\n  pages={1--19},\n  year={2018},\n  publisher={Springer}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Jason Hartford; Devon Graham; Kevin Leyton-Brown; and Siamak Ravanbakhsh.\n\n\n \n \n \n \n \n Deep Models of Interactions Across Sets.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the 35th International Conference on Machine Learning, volume 80, of Proceedings of Machine Learning Research, pages 1909–1918, Jul 2018. PMLR\n \n\n\n\n
\n\n\n\n \n \n \"Deep pdf\n  \n \n \n \"Deep arxiv\n  \n \n \n \"Deep code tensorflow\n  \n \n \n \"Deep code pytorch\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 55 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{pmlr-v80-hartford18a,\n  title = \t {Deep Models of Interactions Across Sets},\n  author = \t {Hartford, Jason and Graham, Devon and Leyton-Brown, Kevin and Ravanbakhsh, Siamak},\n  booktitle = \t {Proceedings of the 35th International Conference on Machine Learning},\n  pages = \t {1909--1918},\n  year = \t {2018},\n  volume = \t {80},\n  series = \t {Proceedings of Machine Learning Research},\n  month = \t {Jul},\n  publisher = \t {PMLR},\n  url_pdf = {http://proceedings.mlr.press/v80/hartford18a/hartford18a-supp.pdf},\n  url_arxiv = {https://arxiv.org/abs/1803.02879},\n  url_code_TensorFlow = {https://github.com/mravanba/deep_exchangeable_tensors},\n  url_code_Pytorch = {https://github.com/jhartford/AutoEncSets},\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Sumedha Singla; Mingming Gong; Siamak Ravanbakhsh; Frank Sciurba; Barnabas Poczos; and Kayhan N Batmanghelich.\n\n\n \n \n \n \n Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector.\n \n \n \n\n\n \n\n\n\n In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 502–510, 2018. Springer\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{singla2018subject2vec,\n  title={Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector},\n  author={Singla, Sumedha and Gong, Mingming and Ravanbakhsh, Siamak and Sciurba, Frank and Poczos, Barnabas and Batmanghelich, Kayhan N},\n  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},\n  pages={502--510},\n  year={2018},\n  organization={Springer}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Siyu He; Siamak Ravanbakhsh; and Shirley Ho.\n\n\n \n \n \n \n \n Analysis of Cosmic Microwave Background with Deep Learning.\n \n \n \n \n\n\n \n\n\n\n In International Conference on Learning Representations (ICLR), workshop track, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"Analysis pdf\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{he_cmb,\n  title={Analysis of Cosmic Microwave Background with Deep Learning},\n  author={He, Siyu and Ravanbakhsh, Siamak and Ho, Shirley},\n  booktitle={International Conference on Learning Representations (ICLR), workshop track},\n  year={2018},\n  url_pdf = {https://openreview.net/pdf?id=B15uoOyvz},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n François Lanusse; Quanbin Ma; Nan Li; Thomas E. Collett; Chun-Liang Li; Siamak Ravanbakhsh; Rachel Mandelbaum; and Barnabás Póczos.\n\n\n \n \n \n \n \n CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding.\n \n \n \n \n\n\n \n\n\n\n Monthly Notices of the Royal Astronomical Society, 473(3): 3895-3906. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"CMUPaper\n  \n \n \n \"CMU in the news\n  \n \n \n \"CMU code\n  \n \n \n \"CMU arxiv\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{doi:10.1093/mnras/stx1665,\nauthor = {Lanusse, François and Ma, Quanbin and Li, Nan and Collett, Thomas E. and Li, Chun-Liang and Ravanbakhsh, Siamak and Mandelbaum, Rachel and Póczos, Barnabás},\ntitle = {CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding},\njournal = {Monthly Notices of the Royal Astronomical Society},\nvolume = {473},\nnumber = {3},\npages = {3895-3906},\nyear = {2018},\ndoi = {10.1093/mnras/stx1665},\nURL = {http://dx.doi.org/10.1093/mnras/stx1665},\nurl_in_the_news={http://www.cmu.edu/mcs/news-events/2017/0512-Strong-Lensing-Challenge.html},\nurl_code={https://github.com/McWilliamsCenter/CMUDeepLens},\nurl_arXiv = {https://arxiv.org/abs/1703.02642},\n}\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 2017\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n  \n undefined\n \n \n (5)\n \n \n
\n
\n \n \n
\n \n\n \n \n Manzil Zaheer; Satwik Kottur; Siamak Ravanbakhsh; Barnabas Poczos; Ruslan R Salakhutdinov; and Alexander J Smola.\n\n\n \n \n \n \n \n Deep Sets.\n \n \n \n \n\n\n \n\n\n\n In Advances in Neural Information Processing Systems 30, pages 3391–3401, 2017. Curran Associates, Inc.\n \n\n\n\n
\n\n\n\n \n \n \"Deep pdf\n  \n \n \n \"Deep supplemental\n  \n \n \n \"Deep arxiv\n  \n \n \n \"Deep code\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 58 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{NIPS2017_6931,\ntitle = {Deep Sets},\nauthor = {Zaheer, Manzil and Kottur, Satwik and Ravanbakhsh, Siamak and Poczos, Barnabas and Salakhutdinov, Ruslan R and Smola, Alexander J},\nbooktitle = {Advances in Neural Information Processing Systems 30},\npages = {3391--3401},\nyear = {2017},\npublisher = {Curran Associates, Inc.},\nurl_pdf = {http://papers.nips.cc/paper/6931-deep-sets.pdf},\nurl_supplemental = {https://papers.nips.cc/paper/6931-deep-sets-supplemental.zip},\nurl_arXiv = {https://arxiv.org/abs/1703.06114},\nurl_code = {https://github.com/manzilzaheer/DeepSets},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Christopher Srinivasa; Inmar Givoni; Siamak Ravanbakhsh; and Brendan J Frey.\n\n\n \n \n \n \n \n Min-Max Propagation.\n \n \n \n \n\n\n \n\n\n\n In Advances in Neural Information Processing Systems 30, pages 5565–5573, 2017. Curran Associates, Inc.\n \n\n\n\n
\n\n\n\n \n \n \"Min-Max pdf\n  \n \n \n \"Min-Max supplemental\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{NIPS2017_7140,\ntitle = {Min-Max Propagation},\nauthor = {Srinivasa, Christopher and Givoni, Inmar and Ravanbakhsh, Siamak and Frey, Brendan J},\nbooktitle = {Advances in Neural Information Processing Systems 30},\npages = {5565--5573},\nyear = {2017},\npublisher = {Curran Associates, Inc.},\nurl_pdf = {http://papers.nips.cc/paper/7140-min-max-propagation.pdf},\nurl_supplemental = {http://papers.nips.cc/paper/7140-min-max-propagation-supplemental.zip}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Siamak Ravanbakhsh; Jeff Schneider; and Barnabas Poczos.\n\n\n \n \n \n \n \n Equivariance Through Parameter-Sharing.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the 34th International Conference on Machine Learning, volume 70, of JMLR: W&CP, August 2017. \n \n\n\n\n
\n\n\n\n \n \n \"Equivariance arxiv\n  \n \n \n \"Equivariance pdf\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 32 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{ravanbakhsh_equivariance,\nauthor={Ravanbakhsh, Siamak and Schneider, Jeff and Poczos, Barnabas},\ntitle={Equivariance Through Parameter-Sharing},\n  booktitle = {Proceedings of the 34th International Conference on Machine Learning},\n  shortbooktitle = {ICML '17},\n  series =  {JMLR: W&CP},\n  Volume={70},\n  year =      {2017},\n  location =  {Sydney, Australia, GB},\n  month =     {August},\n  url_arXiv = {https://arxiv.org/abs/1702.08389},\n  url_pdf = {http://proceedings.mlr.press/v70/ravanbakhsh17a/ravanbakhsh17a-supp.pdf}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Siamak Ravanbakhsh; Francois Lanusse; Rachel Mandelbaum; Jeff Schneider; and Barnabas Poczos.\n\n\n \n \n \n \n \n Enabling Dark Energy Science with Deep Generative Models of Galaxy Images.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the Thirty First AAAI Conference on Artificial Intelligence, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"Enabling arxiv\n  \n \n \n \"Enabling pdf\n  \n \n \n \"Enabling in nature news\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{ravanbakhsh_gengalaxy,\n  title={Enabling Dark Energy Science with Deep Generative Models of Galaxy Images},\n  author={Ravanbakhsh, Siamak and Lanusse, Francois and Mandelbaum, Rachel and Schneider, Jeff and Poczos, Barnabas},\n  booktitle={Proceedings of the Thirty First AAAI Conference on Artificial Intelligence},\n  url_arXiv = {https://arxiv.org/abs/1609.05796},\n  url_pdf = {http://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14765/13939},\n  url_in_Nature_News={http://www.nature.com/news/astronomers-explore-uses-for-ai-generated-images-1.21398},\n  year={2017},\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Siamak Ravanbakhsh; Jeff Schneider; and Barnabas Poczos.\n\n\n \n \n \n \n \n Deep Learning with Sets and Point Clouds.\n \n \n \n \n\n\n \n\n\n\n In International Conference on Learning Representations (ICLR), workshop track, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"Deep arxiv\n  \n \n \n \"Deep code\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{ravanbakhsh_sets,\n  title={Deep Learning with Sets and Point Clouds},\n  author={Ravanbakhsh, Siamak and Schneider, Jeff and Poczos, Barnabas},\n  booktitle={International Conference on Learning Representations (ICLR), workshop track},\n  year={2017},\nurl_arXiv = {https://arxiv.org/abs/1611.04500},\nurl_code = {https://github.com/manzilzaheer/DeepSets},\n}\n\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 2016\n \n \n (3)\n \n \n
\n
\n \n \n
\n
\n  \n 4\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n Siamak Ravanbakhsh; Barnabas Poczos; Jeff Schneider; Dale Schuurmans; and Russell Greiner.\n\n\n \n \n \n \n \n Stochastic Neural Networks with Monotonic Activation Functions.\n \n \n \n \n\n\n \n\n\n\n In International Conference on Artificial Intelligence and Statistics, volume 51, of JMLR: W&CP, pages 809–818, 2016. \n \n\noral presentation (6.5% acceptance rate)\n\n
\n\n\n\n \n \n \"Stochastic pdf\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 25 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@INPROCEEDINGS{ravanbakhsh_exprbm,\n    author = {Ravanbakhsh, Siamak and Poczos, Barnabas and Schneider, Jeff and Schuurmans, Dale and Greiner, Russell},\n    title = {Stochastic Neural Networks with Monotonic Activation Functions},\n    Booktitle  = {International Conference on Artificial Intelligence and Statistics},\n    Shortbooktitle={AISTATS},\n\tSeries = {JMLR: W&CP},\n    order = {4},\n    Volume={51},\n    Pages = {809–818},\n    year = {2016},\n    location = {Cadiz, Spain},\n url_pdf = {http://www.jmlr.org/proceedings/papers/v51/ravanbakhsh16.pdf},\nBibbase_Note={<font color="#E74C3C">oral presentation (6.5\\% acceptance rate)</font>},\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Siamak Ravanbakhsh; Barnabás Póczos; and Russell Greiner.\n\n\n \n \n \n \n \n Boolean Matrix Factorization and Noisy Completion via Message Passing.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of The 33rd International Conference on Machine Learning, volume 48, of JMLR: W&CP, 2016. \n \n\n\n\n
\n\n\n\n \n \n \"Boolean paper\n  \n \n \n \"Boolean code\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 18 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@INPROCEEDINGS{ravanbakhsh_boolean,\n  title = {Boolean Matrix Factorization and Noisy Completion via Message Passing},\n  author = {Ravanbakhsh, Siamak and P{\\'o}czos, Barnab{\\'a}s and Greiner, Russell},\n  booktitle = {Proceedings of The 33rd International Conference on Machine Learning},\n  Shortbooktitle={ICML},\n  Series = {JMLR: W&CP},\n  Volume={48},\n  order = {4},\n  year = {2016},\n  location = {New York, USA},\n  url_Paper = {http://jmlr.org/proceedings/papers/v48/ravanbakhsha16.pdf},\n  url_Code = {https://github.com/mravanba/BooleanFactorization},\n}\n\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 5\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n Siamak Ravanbakhsh; Junier Oliva; Sebastien Fromenteau; Layne C Price; Shirley Ho; Jeff Schneider; and Barnabás Póczos.\n\n\n \n \n \n \n \n Estimating Cosmological Parameters from the Dark Matter Distribution.\n \n \n \n \n\n\n \n\n\n\n In Maria Balcan; and Kilian Weinberger., editor(s), Proceedings of The 33rd International Conference on Machine Learning, volume 48, of JMLR: W&CP, 2016. \n \n\n\n\n
\n\n\n\n \n \n \"Estimating paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 10 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@INPROCEEDINGS{ravanbakhsh_lambdacdm,\n  title={Estimating Cosmological Parameters from the Dark Matter Distribution},\n  author={Ravanbakhsh, Siamak and Oliva, Junier and Fromenteau, Sebastien and Price, Layne C and Ho, Shirley and Schneider, Jeff and P{\\'o}czos, Barnab{\\'a}s},\n  booktitle = {Proceedings of The 33rd International Conference on Machine Learning},\n  editor = {Maria Balcan and Kilian Weinberger},\n  Shortbooktitle={ICML},\n  Series = {JMLR: W&CP},\n  order = {5},\n  Volume={48},\n  year = {2016},\n  location = {New York, USA},\n  url_Paper = {http://jmlr.org/proceedings/papers/v48/ravanbakhshb16.pdf},\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 6\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n Christopher Srinivasa; Siamak Ravanbakhsh; and Brendan Frey.\n\n\n \n \n \n \n \n Survey Propagation beyond Constraint Satisfaction Problems.\n \n \n \n \n\n\n \n\n\n\n In International Conference on Artificial Intelligence and Statistics, volume 51, of JMLR: W&CP, pages 286–295, 2016. \n \n\noral presentation (6.5% acceptance rate)\n\n
\n\n\n\n \n \n \"Survey pdf\n  \n \n \n \"Survey supplemental\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@INPROCEEDINGS{Srinivasa_sp,\n    author = {Srinivasa, Christopher and Ravanbakhsh, Siamak and Frey, Brendan},\n    title = {Survey Propagation beyond Constraint Satisfaction Problems},\n    Booktitle  = {International Conference on Artificial Intelligence and Statistics},\n    Shortbooktitle={AISTATS},\nSeries = {JMLR: W&CP},\n    Volume={51},\n    Pages = {286–295},\n    order = {6},\n    year = {2016},\n    location = {Cadiz, Spain},\nurl_pdf={http://www.jmlr.org/proceedings/papers/v51/srinivasa16.pdf},\nurl_supplemental = {http://proceedings.mlr.press/v51/srinivasa16-supp.pdf},\nBibbase_Note={<font color="#E74C3C">oral presentation (6.5\\% acceptance rate)</font>},\n}\n\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 2015\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n  \n undefined\n \n \n (3)\n \n \n
\n
\n \n \n
\n \n\n \n \n Farzaneh Mirzazadeh; Siamak Ravanbakhsh; Nan Ding; and Dale Schuurmans.\n\n\n \n \n \n \n \n Embedding Inference for Structured Multilabel Prediction.\n \n \n \n \n\n\n \n\n\n\n In C. Cortes; N. D. Lawrence; D. Lee; M. Sugiyama; and R. Garnett., editor(s), Advances in Neural Information Processing Systems 28, pages 3555–3563. Curran Associates, Inc., 2015.\n \n\n\n\n
\n\n\n\n \n \n \"Embedding pdf\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@incollection{NIPS2015_5675,\ntitle = {Embedding Inference for Structured Multilabel Prediction},\nauthor = {Mirzazadeh, Farzaneh and Ravanbakhsh, Siamak and Ding, Nan and Schuurmans, Dale},\nbooktitle = {Advances in Neural Information Processing Systems 28},\neditor = {C. Cortes and N. D. Lawrence and D. D. Lee and M. Sugiyama and R. Garnett},\npages = {3555--3563},\nyear = {2015},\npublisher = {Curran Associates, Inc.},\nurl_pdf = {http://papers.nips.cc/paper/5675-embedding-inference-for-structured-multilabel-prediction.pdf}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Siamak Ravanbakhsh; and Russell Greiner.\n\n\n \n \n \n \n \n Perturbed Message Passing for Constraint Satisfaction Problems.\n \n \n \n \n\n\n \n\n\n\n Journal of Machine Learning Research, 16: 1249-1274. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"Perturbed pdf\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{ravanbakhsh_csp,\n  title={Perturbed Message Passing for Constraint Satisfaction Problems},\n  author={Ravanbakhsh, Siamak and Greiner, Russell},\n  journal={Journal of Machine Learning Research},\n  volume={16},\n  pages={1249-1274},\n  year={2015},\n  url_pdf = {http://www.jmlr.org/papers/volume16/ravanbakhsh15a/ravanbakhsh15a.pdf},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Siamak Ravanbakhsh; Philip Liu; Trent Bjordahl; Rupasri Mandal; Jason Grant; Michael Wilson; Roman Eisner; Igor Sinelnikov; Xiaoyu Hu; Claudio Luchinat; Russell Greiner; and David Wishart.\n\n\n \n \n \n \n \n Accurate, Fully-Automated NMR Spectral Profiling for Metabolomics.\n \n \n \n \n\n\n \n\n\n\n PLoS ONE, 10(5): e0124219. 05 2015.\n \n\n\n\n
\n\n\n\n \n \n \"Accurate, arxiv\n  \n \n \n \"Accurate, pdf\n  \n \n \n \"Accurate, website\n  \n \n \n \"Accurate, in the news\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{ravanbakhsh_plosone,\n    author = {Ravanbakhsh, Siamak and Liu, Philip and Bjordahl, Trent  and Mandal, Rupasri and Grant, Jason  and Wilson, Michael and Eisner, Roman and Sinelnikov, Igor and Hu, Xiaoyu and Luchinat, Claudio and Greiner, Russell and Wishart, David },\n    journal = {PLoS ONE},\n    publisher = {Public Library of Science},\n    title = {Accurate, Fully-Automated NMR Spectral Profiling for Metabolomics},\n    year = {2015},\n    month = {05},\n    volume = {10},\n    pages = {e0124219},\n    number = {5},\n    doi = {10.1371/journal.pone.0124219},\n    url_arXiv = {https://arxiv.org/abs/1409.1456},\n\turl_pdf = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0124219},\n\turl_WebSite = {http://bayesil.ca/},\n  url_in_the_news = {https://www.ualberta.ca/news-and-events/newsarticles/2015/may/machine-learning-breakthrough-could-revolutionize-medicine},\n}\n\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 2014\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n  \n undefined\n \n \n (3)\n \n \n
\n
\n \n \n
\n \n\n \n \n Siamak Ravanbakhsh; Reihaneh Rabbany; and Russell Greiner.\n\n\n \n \n \n \n \n Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning.\n \n \n \n \n\n\n \n\n\n\n In Advances in Neural Information Processing Systems, pages 289–297, Cambridge, MA, USA, 2014. MIT Press\n \n\n\n\n
\n\n\n\n \n \n \"Augmentative paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{ravanbakhsh_tsp,\n author = {Ravanbakhsh, Siamak and Rabbany, Reihaneh and Greiner, Russell},\n title = {Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning},\n booktitle = {Advances in Neural Information Processing Systems},\n year = {2014},\n location = {Montreal, Canada},\n pages = {289--297},\n numpages = {9},\n acmid = {2968859},\n publisher = {MIT Press},\n address = {Cambridge, MA, USA},\n url_Paper = {https://papers.nips.cc/paper/5601-augmentative-message-passing-for-traveling-salesman-problem-and-graph-partitioning.pdf},\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Siamak Ravanbakhsh; Christopher Srinivasa; Brendan Frey; and Russell Greiner.\n\n\n \n \n \n \n \n Min-Max Problems on Factor Graphs.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the 31st International Conference on Machine Learning, pages 1035–1043, 2014. \n \n\n\n\n
\n\n\n\n \n \n \"Min-Max pdf\n  \n \n \n \"Min-Max supplement\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{ravanbakhsh_minmax,\n  title={Min-Max Problems on Factor Graphs},\n  author={Ravanbakhsh, Siamak and Srinivasa, Christopher and Frey, Brendan and Greiner, Russell},\n  booktitle={Proceedings of the 31st International Conference on Machine Learning},\n  pages={1035--1043},\n  year={2014},\n  keywords={inference,message passing,combinatorial optimization,application},\n  url_pdf = {http://jmlr.org/proceedings/papers/v32/ravanbakhsh14.pdf},\n  url_supplement = {http://proceedings.mlr.press/v32/ravanbakhsh14-supp.pdf}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Siamak Ravanbakhsh; Russell Greiner; and Brendan Frey.\n\n\n \n \n \n \n \n Training Restricted Boltzmann Machine by Perturbation.\n \n \n \n \n\n\n \n\n\n\n In NIPS:workshop on perturbation, optimization and statistics, 2014. \n \n\n\n\n
\n\n\n\n \n \n \"Training pdf\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{ravanbakhsh_pmrbm,\n  title={Training Restricted Boltzmann Machine by Perturbation},\n  author={Ravanbakhsh, Siamak and Greiner, Russell and Frey, Brendan},\n  booktitle={NIPS:workshop on perturbation, optimization and statistics},\n  location={lake Tahoe, USA},\n  url_pdf = {https://arxiv.org/pdf/1405.1436v1.pdf},\n  year={2014},\n}\n\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 2013\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n  \n undefined\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n Siamak Ravanbakhsh; Melissa Gajewski; Russell Greiner; and Jack A Tuszynski.\n\n\n \n \n \n \n \n Determination of the optimal tubulin isotype target as a method for the development of individualized cancer chemotherapy.\n \n \n \n \n\n\n \n\n\n\n Theoretical Biology and Medical Modelling, 10(1): 1. 2013.\n \n\n\n\n
\n\n\n\n \n \n \"Determination pdf\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{ravanbakhsh_tubulin,\n  title={Determination of the optimal tubulin isotype target as a method for the development of individualized cancer chemotherapy},\n  author={Ravanbakhsh, Siamak and Gajewski, Melissa and Greiner, Russell and Tuszynski, Jack A},\n  journal={Theoretical Biology and Medical Modelling},\n  volume={10},\n  number={1},\n  pages={1},\n  year={2013},\n  publisher={BioMed Central},\n  url_pdf = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3651705/pdf/1742-4682-10-29.pdf},\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 2012\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n  \n 10\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n Siamak Ravanbakhsh; Chun-Nam Yu; and Russell Greiner.\n\n\n \n \n \n \n \n A Generalized Loop Correction Method for Approximate Inference in Graphical Models.\n \n \n \n \n\n\n \n\n\n\n In John Langford; and Joelle Pineau., editor(s), Proceedings of the 29th International Conference on Machine Learning, of ICML '12, pages 543–550, New York, NY, USA, July 2012. Omnipress\n \n\n\n\n
\n\n\n\n \n \n \"A pdf\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{ravanbakhsh_glc,\n  author =    {Siamak Ravanbakhsh and Chun-Nam Yu and Russell Greiner},\n  title =     {A Generalized Loop Correction Method  for Approximate Inference in Graphical Models},\n  booktitle = {Proceedings of the 29th International Conference on Machine Learning},\n  series =    {ICML '12},\n  year =      {2012},\n  editor =    {John Langford and Joelle Pineau},\n  location =  {Edinburgh, Scotland, GB},\n  isbn =      {978-1-4503-1285-1},\n  month =     {July},\n  order = {10},\n  publisher = {Omnipress},\n  address =   {New York, NY, USA},\n  pages=      {543--550},\n  url_pdf = {http://icml.cc/2012/papers/304.pdf},\n}\n\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n
\n
\n  \n 2010\n \n \n (1)\n \n \n
\n
\n \n \n
\n
\n  \n 100\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n Siamak Ravanbakhsh; Barnabas Poczos; and Russ Greiner.\n\n\n \n \n \n \n \n A Cross Entropy Optimization Method for Partially Decomposable Problems.\n \n \n \n \n\n\n \n\n\n\n In D. Poole M. Fox., editor(s), Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence. Special Track on AI and Bioinformatics, pages 1280–1286, Atlanta, USA, July 11 – 15 2010. AAAI Press\n \n\n\n\n
\n\n\n\n \n \n \"A paper\n  \n \n \n \"A slides\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{ravanbakhsh_nmr,\n  author    = {Ravanbakhsh, Siamak and Poczos, Barnabas and Greiner, Russ},\n  title     = {A Cross Entropy Optimization Method for Partially Decomposable Problems},\n  booktitle = {Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence. Special Track on AI and Bioinformatics},\n  pages     = {1280--1286},\n  year      = {2010},\n  editor    = {M. Fox, D. Poole},\n  publisher = {AAAI Press},\n  month     = {July 11 {--} 15 },\n  address   = {Atlanta, USA},\n  order = {100},\n  url_Paper = {http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1848/2196},\n  url_Slides = {http://www.cs.ubc.ca/~siamakx/presentations/AAAI_ceed_presentation.pdf},\n}\n\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n
\n\n\n\n\n
\n\n\n \n\n \n \n \n \n\n
\n"}; document.write(bibbase_data.data);