| Wide & deep learning for recommender systems HT Cheng, L Koc, J Harmsen, T Shaked, T Chandra, H Aradhye, ... Proceedings of the 1st workshop on deep learning for recommender systems, 7-10, 2016 | 3834 | 2016 |
| Steganalysis of additive-noise modelable information hiding JJ Harmsen, WA Pearlman Security and Watermarking of Multimedia Contents V 5020, 131-142, 2003 | 631 | 2003 |
| Tensorflow-serving: Flexible, high-performance ml serving C Olston, N Fiedel, K Gorovoy, J Harmsen, L Lao, F Li, V Rajashekhar, ... arXiv preprint arXiv:1712.06139, 2017 | 308 | 2017 |
| Scaling vision transformers to 22 billion parameters M Dehghani, J Djolonga, B Mustafa, P Padlewski, J Heek, J Gilmer, ... International Conference on Machine Learning, 7480-7512, 2023 | 268 | 2023 |
| Network node ad targeting T Rohan, TJ Tunguz-Zawislak, SG Sheffer, J Harmsen US Patent 8,438,062, 2013 | 220 | 2013 |
| et almbox. 2016. Wide & deep learning for recommender systems HT Cheng, L Koc, J Harmsen, T Shaked, T Chandra, H Aradhye, ... Proceedings of the 1st workshop on deep learning for recommender systems. ACM, 0 | 82 | |
| Related entity content identification T Rohan, TJ Tunguz-Zawislak, J Harmsen, S Sundsdal, TM Annau, ... US Patent App. 11/694,345, 2008 | 63 | 2008 |
| Open profile content identification M Nance, M Datar, J Tung, B Rabii, JC Miller, M Hochberg, J Harmsen, ... US Patent 7,730,017, 2010 | 61 | 2010 |
| Uvim: A unified modeling approach for vision with learned guiding codes A Kolesnikov, A Susano Pinto, L Beyer, X Zhai, J Harmsen, N Houlsby Advances in Neural Information Processing Systems 35, 26295-26308, 2022 | 51 | 2022 |
| Higher-order statistical steganalysis of palette images J Harmsen, W Pearlman Proc. SPIE Security Watermarking Multimedia Contents 5020, 131-142, 2003 | 48 | 2003 |
| Up next: retrieval methods for large scale related video suggestion M Bendersky, L Garcia-Pueyo, J Harmsen, V Josifovski, D Lepikhin Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 46 | 2014 |
| Capacity of steganographic channels JJ Harmsen, WA Pearlman Proceedings of the 7th workshop on Multimedia and security, 11-24, 2005 | 41 | 2005 |
| Sibyl: A system for large scale supervised machine learning K Canini, T Chandra, E Ie, J McFadden, K Goldman, M Gunter, J Harmsen, ... Technical Talk 1 (113), 2.3, 2012 | 39 | 2012 |
| Kernel fisher discriminant for steganalysis of JPEG hiding methods JJ Harmsen, WA Pearlman Security, Steganography, and Watermarking of Multimedia Contents VI 5306, 13-22, 2004 | 37 | 2004 |
| Batching inputs to a machine learning model N Fiedel, C Olston, J Harmsen US Patent 10,789,544, 2020 | 34 | 2020 |
| Wide and deep machine learning models T Shaked, R Anil, HB Aradhye, G Anderson, W Chai, ML Koc, J Harmsen, ... US Patent 10,762,422, 2020 | 32 | 2020 |
| Open profile content identification M Nance, M Datar, J Tung, B Rabii, JC Miller, M Hochberg, J Harmsen, ... US Patent 8,341,169, 2012 | 32 | 2012 |
| Fast additive noise steganalysis JJ Harmsen, KD Bowers, WA Pearlman Security, Steganography, and Watermarking of Multimedia Contents VI 5306 …, 2004 | 32 | 2004 |
| User-targeted advertising M Datar, JC Miller, M Hochberg, B Rabii, M Nance, J Tung, J Harmsen, ... US Patent App. 12/025,239, 2009 | 23 | 2009 |
| Custodian based content identification M Nance, M Datar, J Tung, B Rabii, JC Miller, M Hochberg, J Harmsen, ... US Patent 8,321,462, 2012 | 18 | 2012 |
Tushar ChandraGoogleVerified email at google.com
