no code implementations • 1 Oct 2023 • Zui Chen, Lei Cao, Sam Madden, Tim Kraska, Zeyuan Shang, Ju Fan, Nan Tang, Zihui Gu, Chunwei Liu, Michael Cafarella
As a result, data scientists often have to develop domain-specific solutions tailored to both the dataset and the task, e. g. writing domain-specific code or training machine learning models on a sufficient number of annotated examples.
no code implementations • 20 Jun 2023 • Zui Chen, Lei Cao, Sam Madden
In addition to the row-based architecture, we introduce several techniques: cell-aware position embedding, teacher-student training paradigm, and selective backward to improve the performance of RoTaR model.
no code implementations • 20 Jun 2023 • Zui Chen, Lei Cao, Sam Madden
Data curation is a wide-ranging area which contains many critical but time-consuming data processing tasks.
1 code implementation • 15 Jun 2023 • Zihui Gu, Ju Fan, Nan Tang, Songyue Zhang, Yuxin Zhang, Zui Chen, Lei Cao, Guoliang Li, Sam Madden, Xiaoyong Du
PLMs can perform well in schema alignment but struggle to achieve complex reasoning, while LLMs is superior in complex reasoning tasks but cannot achieve precise schema alignment.
1 code implementation • NeurIPS 2021 • Favyen Bastani, Songtao He, Sam Madden
In this paper, we propose a self-supervised learning procedure for training a robust multi-object tracking (MOT) model given only unlabeled video.
no code implementations • 13 Oct 2021 • Favyen Bastani, Songtao He, Satvat Jagwani, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden, Mohammad Amin Sadeghi
To address this challenge, much work has studied automatically processing geospatial data sources such as GPS trajectories and satellite images to reduce the cost of maintaining digital maps.
1 code implementation • ICCV 2021 • Favyen Bastani, Sam Madden
The increasing availability of satellite and aerial imagery has sparked substantial interest in automatically updating street maps by processing aerial images.
no code implementations • 24 Mar 2021 • Songtao He, Favyen Bastani, Mohammad Alizadeh, Hari Balakrishnan, Michael Cafarella, Tim Kraska, Sam Madden
We show TagMe can produce high-quality object annotations in a fully-automatic and low-cost way.
no code implementations • 4 Dec 2020 • Nan Tang, Ju Fan, Fangyi Li, Jianhong Tu, Xiaoyong Du, Guoliang Li, Sam Madden, Mourad Ouzzani
RPT is pre-trained for a tuple-to-tuple model by corrupting the input tuple and then learning a model to reconstruct the original tuple.
no code implementations • 2 Oct 2019 • Favyen Bastani, Songtao He, Satvat Jagwani, Edward Park, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden, Mohammad Amin Sadeghi
Through an evaluation on a large-scale dataset including satellite imagery, GPS trajectories, and ground-truth map data in forty cities, we show that Mapster makes automation practical for map editing, and enables the curation of map datasets that are more complete and up-to-date at less cost.
no code implementations • 17 Jun 2019 • Favyen Bastani, Songtao He, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden
Systems to automatically infer road network graphs from aerial imagery and GPS trajectories have been proposed to improve coverage of road maps.
no code implementations • 14 Jul 2018 • Jeremy Kepner, Ron Brightwell, Alan Edelman, Vijay Gadepally, Hayden Jananthan, Michael Jones, Sam Madden, Peter Michaleas, Hamed Okhravi, Kevin Pedretti, Albert Reuther, Thomas Sterling, Mike Stonebraker
In this context, an operating system can be viewed as software that brokers and tracks the resources of the compute engines and is akin to a database management system.
Distributed, Parallel, and Cluster Computing Databases Operating Systems Performance
1 code implementation • CVPR 2018 • Favyen Bastani, Songtao He, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden, David DeWitt
Mapping road networks is currently both expensive and labor-intensive.