Author of the publication

Yes we can: simplex volume maximization for descriptive web-scale matrix factorization.

, , and . CIKM, page 1785-1788. ACM, (2010)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

 

Other publications of authors with the same name

Effectively creating weakly labeled training examples via approximate domain knowledge, , , , , and . Inductive Logic Programming, Springer, (2015)Kernelized Map Matching for noisy trajectories, and . Proceedings of LWA2010 - Workshop-Woche: Lernen, Wissen & Adaptivitaet, Kassel, Germany, (2010)Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models, , , , , , and . Proceedings of ECML, (2010)Statistical Relational Artificial Intelligence: Logic, Probability, and Computation, , , and . Synthesis Lectures on Artificial Intelligence and Machine Learning Morgan & Claypool Publishers, (2016)Statistical Relational Artificial Intelligence: Logic, Probability, and Computation, , , and . Synthesis Lectures on Artificial Intelligence and Machine Learning Morgan & Claypool, San Rafael, CA, (2016)Using Commonsense Knowledge to Automatically Create (Noisy) Training Examples from Text, , , , , and . Statistical Relational Artificial Intelligence, Papers from the 2013 AAAI Workshop, Bellevue, Washington, USA, July 15, 2013, volume WS-13-16 of AAAI Workshops, AAAI, (2013)Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures., , , , , , and . CoRR, (2019)Faster Attend-Infer-Repeat with Tractable Probabilistic Models., , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 5966-5975. PMLR, (2019)Graph Enhanced Memory Networks for Sentiment Analysis., , and . ECML/PKDD (1), volume 10534 of Lecture Notes in Computer Science, page 374-389. Springer, (2017)Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks, , and . (2019)cite arxiv:1907.06732Comment: 12 Pages, 6 Figures.