1 code implementation • 7 Oct 2023 • Ferdinand Kossmann, Ziniu Wu, Eugenie Lai, Nesime Tatbul, Lei Cao, Tim Kraska, Samuel Madden
We find that no current system sufficiently fulfills both needs and therefore propose Skyscraper, a system tailored to V-ETL.
no code implementations • 2 Jun 2023 • Jiaming Liang, Lei Cao, Samuel Madden, Zachary Ives, Guoliang Li
Timeseries analytics is of great importance in many real-world applications.
no code implementations • 11 Mar 2023 • Yu Wang, Lei Cao, Yizhou Yan, Samuel Madden
Moreover, to effectively handle high dimensional, highly complex data sets which are hard to summarize with simple rules, we propose a localized STAIR approach, called L-STAIR.
no code implementations • 11 Dec 2022 • Ziniu Wu, Parimarjan Negi, Mohammad Alizadeh, Tim Kraska, Samuel Madden
Neither classical nor learning-based methods yield satisfactory performance when estimating the cardinality of the join queries.
no code implementations • ICCV 2021 • Songtao He, Mohammad Amin Sadeghi, Sanjay Chawla, Mohammad Alizadeh, Hari Balakrishnan, Samuel Madden
Traffic accidents cost about 3% of the world's GDP and are the leading cause of death in children and young adults.
1 code implementation • ECCV 2020 • Songtao He, Favyen Bastani, Satvat Jagwani, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Mohamed M. Elshrif, Samuel Madden, Amin Sadeghi
Inferring road graphs from satellite imagery is a challenging computer vision task.
1 code implementation • 28 Apr 2020 • Alana Marzoev, Samuel Madden, M. Frans Kaashoek, Michael Cafarella, Jacob Andreas
Large, human-annotated datasets are central to the development of natural language processing models.
1 code implementation • 28 Dec 2019 • Songtao He, Favyen Bastani, Satvat Jagwani, Edward Park, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Samuel Madden, Mohammad Amin Sadeghi
The usage of graph neural networks allows information propagation on the road network graph and eliminates the receptive field limitation of image classifiers.
no code implementations • 25 Sep 2019 • Lei Cao, Yizhou Yan, Samuel Madden, Elke Rundensteiner
Unfortunately, although the strong generalization ability of existing CNNs ensures their accuracy when classifying known objects, it also causes them to often assign an unknown to a target class with high confidence.
no code implementations • 25 Sep 2019 • Yizhou Yan, Lei Cao, Samuel Madden, Elke Rundensteiner
Although the state-of-the-art object detection methods are successful in detecting and classifying objects by leveraging deep convolutional neural networks (CNNs), these methods overlook the semantic context which implies the probabilities that different classes of objects occur jointly.
no code implementations • ICLR 2019 • Lei Cao, Yizhou Yan, Samuel Madden, Elke Rundensteiner
Modern applications from Autonomous Vehicles to Video Surveillance generate massive amounts of image data.
no code implementations • 29 Mar 2019 • Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael. I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
1 code implementation • 8 Mar 2019 • John K. Feser, Samuel Madden, Nan Tang, Armando Solar-Lezama
Optimizing the physical data storage and retrieval of data are two key database management problems.
Programming Languages Databases
no code implementations • 10 Jun 2018 • Guillaume Leclerc, Manasi Vartak, Raul Castro Fernandez, Tim Kraska, Samuel Madden
As neural networks become widely deployed in different applications and on different hardware, it has become increasingly important to optimize inference time and model size along with model accuracy.
no code implementations • 17 Sep 2012 • Barzan Mozafari, Purnamrita Sarkar, Michael J. Franklin, Michael. I. Jordan, Samuel Madden
Based on this observation, we present two new active learning algorithms to combine humans and algorithms together in a crowd-sourced database.