Search Results for author: Samuel Madden

Found 16 papers, 5 papers with code

A Declarative System for Optimizing AI Workloads

no code implementations23 May 2024 Chunwei Liu, Matthew Russo, Michael Cafarella, Lei Cao, Peter Baille Chen, Zui Chen, Michael Franklin, Tim Kraska, Samuel Madden, Gerardo Vitagliano

In this paper we present Palimpzest, a system that enables anyone to process AI-powered analytical queries simply by defining them in a declarative language.

Extract-Transform-Load for Video Streams

1 code implementation7 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.

Self-Driving Cars

RITA: Group Attention is All You Need for Timeseries Analytics

no code implementations2 Jun 2023 Jiaming Liang, Lei Cao, Samuel Madden, Zachary Ives, Guoliang Li

Timeseries analytics is of great importance in many real-world applications.

Interpretable Outlier Summarization

no code implementations11 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.

Anomaly Detection Outlier Detection

FactorJoin: A New Cardinality Estimation Framework for Join Queries

no code implementations11 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.


RoadTagger: Robust Road Attribute Inference with Graph Neural Networks

1 code implementation28 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.


Context-Aware Object Detection With Convolutional Neural Networks

no code implementations25 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.

Object object-detection +1

Unknown-Aware Deep Neural Network

no code implementations25 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.

Image Classification

Deductive Optimization of Relational Data Storage

1 code implementation8 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

Smallify: Learning Network Size while Training

no code implementations10 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.

Active Learning for Crowd-Sourced Databases

no code implementations17 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.

Active Learning BIG-bench Machine Learning

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