Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Sahaana Suri"'
Autor:
Sahaana Suri, Eric Xu, Edward Gan, Peter Kraft, Asvin Ananthanarayan, Xi Wu, Firas Abuzaid, John Te-Jui Sheu, Matei Zaharia, Erik Meijer, Peter Bailis, Atul Shenoy, Jeffrey F. Naughton
Publikováno v:
The VLDB Journal. 30:45-70
A range of explanation engines assist data analysts by performing feature selection over increasingly high-volume and high-dimensional data, grouping and highlighting commonalities among data points. While useful in diverse tasks such as user behavio
Structured data, or data that adheres to a pre-defined schema, can suffer from fragmented context: information describing a single entity can be scattered across multiple datasets or tables tailored for specific business needs, with no explicit linki
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0cc48280d72073109d805a135b1caf0
Autor:
Sahaana Suri, Peter Bailis, Sugato Basu, Yemao Zeng, Abishek Sethi, Raghuveer Chanda, Girija Narlikar, Christopher Ré, Pradyumna Narayana, Neslihan Bulut
As applications in large organizations evolve, the machine learning (ML) models that power them must adapt the same predictive tasks to newly arising data modalities (e.g., a new video content launch in a social media application requires existing te
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::52a379b278e7935766fed0f090e8eb77
Autor:
Matthew Weiner, Sahaana Suri, Vasuki Narasimha Swamy, Gireeja Ranade, Borivoje Nikolic, Paul Rigge, Anant Sahai
Publikováno v:
IEEE Transactions on Wireless Communications. 16:7168-7183
High-performance industrial automation systems rely on tens of simultaneously active sensors and actuators and have stringent communication latency and reliability requirements. Current wireless technologies like WiFi, Bluetooth, and LTE are unable t
Publikováno v:
Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning.
Domain adaptation provides a powerful set of model training techniques given domain-specific training data and supplemental data with unknown relevance. The techniques are useful when users need to develop models with data from varying sources, of va
Autor:
Sahaana Suri, Peter Bailis
Publikováno v:
DEEM@SIGMOD
Dimensionality reduction (DR) is critical in scaling machine learning pipelines: by reducing input dimensionality in exchange for a preprocessing overhead, DR enables faster end-to-end runtime. Principal component analysis (PCA) is a DR standard, but
Publikováno v:
SIGMOD Conference
Data volumes are rising at an increasing rate, stressing the limits of human attention. Current techniques for prioritizing user attention in this fast data are characterized by either cumbersome, ad-hoc analysis pipelines comprised of a diverse set
Autor:
Sahaana Suri, Borivoje Nikolic, Matthew Weiner, Vasuki Narasimha Swamy, Paul Rigge, Anant Sahai, Gireeja Ranade
Publikováno v:
ICC
The Internet of Things envisions not only sensing but also actuation of numerous wirelessly connected devices. Seamless control with humans in the loop requires latencies on the order of a millisecond with very high reliabilities, paralleling the req