J
Jeremiah Harmsen
Researcher at Google
Publications - 22
Citations - 4896
Jeremiah Harmsen is an academic researcher from Google. The author has contributed to research in topics: Computer science & Steganalysis. The author has an hindex of 14, co-authored 20 publications receiving 3651 citations. Previous affiliations of Jeremiah Harmsen include Rensselaer Polytechnic Institute.
Papers
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Proceedings ArticleDOI
Wide & Deep Learning for Recommender Systems
Heng-Tze Cheng,Levent Koc,Jeremiah Harmsen,Tal Shaked,Tushar Deepak Chandra,Hrishi Aradhye,Glen Anderson,Greg S. Corrado,Wei Chai,Mustafa Ispir,Rohan Anil,Zakaria Haque,Lichan Hong,Vihan Jain,Xiaobing Liu,Hemal Shah +15 more
TL;DR: Wide & Deep learning is presented---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems and is open-sourced in TensorFlow.
Posted Content
Wide & Deep Learning for Recommender Systems
Heng-Tze Cheng,Levent Koc,Jeremiah Harmsen,Tal Shaked,Tushar Deepak Chandra,Hrishi Aradhye,Glen Anderson,Greg S. Corrado,Wei Chai,Mustafa Ispir,Rohan Anil,Zakaria Haque,Lichan Hong,Vihan Jain,Xiaobing Liu,Hemal Shah +15 more
TL;DR: Wide & Deep as mentioned in this paper combines the benefits of memorization and generalization for recommender systems by jointly trained wide linear models and deep neural networks, which can generalize better to unseen feature combinations through lowdimensional dense embeddings learned for the sparse features.
Proceedings ArticleDOI
Steganalysis of additive-noise modelable information hiding
TL;DR: In this article, it is shown that these embedding methods are equivalent to a lowpass filtering of histograms that is quantified by a decrease in the HCF center of mass (COM), which is exploited in known scheme detection to classify unaltered and spread spectrum images using a bivariate classifier.
Posted Content
TensorFlow-Serving: Flexible, High-Performance ML Serving
Christopher Olston,Fangwei Li,Jeremiah Harmsen,Jordan Soyke,Kiril Gorovoy,Li Lao,Noah Fiedel,Sukriti Ramesh,Vinu Rajashekhar +8 more
TL;DR: TensorFlow-Serving is described, a system to serve machine learning models inside Google which is also available in the cloud and via open-source, and ways to integrate with systems that convey new models and updated versions from training to serving.
Patent
Network node ad targeting
TL;DR: In this article, a computer-implemented method for displaying advertisements to members of a network comprises identifying one or more communities of members, identifying the influencers in the communities, and placing advertisements at the profiles of the members in the identified communities.