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Found 15 papers, 5 papers with code
Active Learning for Crowd-Sourced Databases
Based on this observation, we present two new active learning algorithms to combine humans and algorithms together in a crowd-sourced database.
Smallify: Learning Network Size while Training
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.
Deductive Optimization of Relational Data Storage
Optimizing the physical data storage and retrieval of data are two key database management problems.
Programming Languages Databases
MLSys: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
Outlier Detection from Image Data
Modern applications from Autonomous Vehicles to Video Surveillance generate massive amounts of image data.
Context-Aware Object Detection With Convolutional Neural Networks
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.
Unknown-Aware Deep Neural Network
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.
RoadTagger: Robust Road Attribute Inference with Graph Neural Networks
The usage of graph neural networks allows information propagation on the road network graph and eliminates the receptive field limitation of image classifiers.
Unnatural Language Processing: Bridging the Gap Between Synthetic and Natural Language Data
Large, human-annotated datasets are central to the development of natural language processing models.
Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding
Inferring road graphs from satellite imagery is a challenging computer vision task.
Inferring High-Resolution Traffic Accident Risk Maps Based on Satellite Imagery and GPS Trajectories
Traffic accidents cost about 3% of the world's GDP and are the leading cause of death in children and young adults.
FactorJoin: A New Cardinality Estimation Framework for Join Queries
Neither classical nor learning-based methods yield satisfactory performance when estimating the cardinality of the join queries.
Interpretable Outlier Summarization
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.
RITA: Group Attention is All You Need for Timeseries Analytics
Timeseries analytics is of great importance in many real-world applications.
Extract-Transform-Load for Video Streams
We find that no current system sufficiently fulfills both needs and therefore propose Skyscraper, a system tailored to V-ETL.
