Sanjay Krishnan | DeepAI

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∙ 09/11/2023

Quantifying Uncertainty in Aggregate Queries over Integrated Datasets

Data integration is a notoriously difficult and heuristic-driven process...

0 Deniz Turkcapar, et al. ∙

∙ 06/02/2023

DeepScribe: Localization and Classification of Elamite Cuneiform Signs Via Deep Learning

Twenty-five hundred years ago, the paperwork of the Achaemenid Empire wa...

0 Edward C. Williams, et al. ∙

∙ 05/05/2023

Data Station: Delegated, Trustworthy, and Auditable Computation to Enable Data-Sharing Consortia with a Data Escrow

Pooling and sharing data increases and distributes its value. But since ...

0 Siyuan Xia, et al. ∙

∙ 03/02/2023

EdgeServe: An Execution Layer for Decentralized Prediction

The relevant features for a machine learning task may be aggregated from...

0 Ted Shaowang, et al. ∙

∙ 04/20/2022

JanusAQP: Efficient Partition Tree Maintenance for Dynamic Approximate Query Processing

Approximate query processing over dynamic databases, i.e., under inserti...

0 Xi Liang, et al. ∙

∙ 12/07/2021

VizExtract: Automatic Relation Extraction from Data Visualizations

Visual graphics, such as plots, charts, and figures, are widely used to ...

0 Dale Decatur, et al. ∙

∙ 10/05/2021

Version Reconciliation for Collaborative Databases

We propose MindPalace, a prototype of a versioned database for efficient...

0 Nalin Ranjan, et al. ∙

∙ 03/29/2021

Combining Aggregation and Sampling (Nearly) Optimally for Approximate Query Processing

Sample-based approximate query processing (AQP) suffers from many pitfal...

0 Xi Liang, et al. ∙

∙ 02/23/2021

CIAO: An Optimization Framework for Client-Assisted Data Loading

Data loading has been one of the most common performance bottlenecks for...

0 Cong Ding, et al. ∙

∙ 08/31/2020

The Data Station: Combining Data, Compute, and Market Forces

This paper introduces Data Stations, a new data architecture that we are...

0 Raul Castro Fernandez, et al. ∙

∙ 04/08/2020

Fast and Reliable Missing Data Contingency Analysis with Predicate-Constraints

Today, data analysts largely rely on intuition to determine whether miss...

0 Xi Liang, et al. ∙

∙ 03/18/2020

Machine Learning enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios

The application of Machine Learning (ML) techniques to complex engineeri...

0 Adam Dziedzic, et al. ∙

∙ 02/08/2020

An Empirical Evaluation of Perturbation-based Defenses

Recent work has extensively shown that randomized perturbations of a neu...

0 Adam Dziedzic, et al. ∙

∙ 11/21/2019

Machine Learning based detection of multiple Wi-Fi BSSs for LTE-U CSAT

According to the LTE-U Forum specification, a LTE-U base-station (BS) re...

0 Vanlin Sathya, et al. ∙

∙ 11/21/2019

Band-limited Training and Inference for Convolutional Neural Networks

The convolutional layers are core building blocks of neural network arch...

0 Adam Dziedzic, et al. ∙

∙ 05/10/2019

Selectivity Estimation with Deep Likelihood Models

Selectivity estimation has long been grounded in statistical tools for d...

0 Zongheng Yang, et al. ∙

∙ 04/26/2019

AlphaClean: Automatic Generation of Data Cleaning Pipelines

The analyst effort in data cleaning is gradually shifting away from the ...

0 Sanjay Krishnan, et al. ∙

∙ 03/04/2019

Opportunistic View Materialization with Deep Reinforcement Learning

Carefully selected materialized views can greatly improve the performanc...

0 Xi Liang, et al. ∙

∙ 12/18/2018

DeepLens: Towards a Visual Data Management System

Advances in deep learning have greatly widened the scope of automatic co...

0 Sanjay Krishnan, et al. ∙

∙ 11/19/2018

Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models

Generalizing manipulation skills to new situations requires extracting i...

0 Ajay Kumar Tanwani, et al. ∙

∙ 08/09/2018

Learning to Optimize Join Queries With Deep Reinforcement Learning

Exhaustive enumeration of all possible join orders is often avoided, and...

0 Sanjay Krishnan, et al. ∙

∙ 12/08/2017

SPRK: A Low-Cost Stewart Platform For Motion Study In Surgical Robotics

To simulate body organ motion due to breathing, heart beats, or peristal...

0 Vatsal Patel, et al. ∙

∙ 12/08/2017

Using Intermittent Synchronization to Compensate for Rhythmic Body Motion During Autonomous Surgical Cutting and Debridement

Anatomical structures are rarely static during a surgical procedure due ...

0 Vatsal Patel, et al. ∙

∙ 11/04/2017

Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning

Rather than learning new control policies for each new task, it is possi...

0 Richard Liaw, et al. ∙

∙ 11/03/2017

BoostClean: Automated Error Detection and Repair for Machine Learning

Predictive models based on machine learning can be highly sensitive to d...

0 Sanjay Krishnan, et al. ∙

∙ 10/15/2017

DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrations

An option is a short-term skill consisting of a control policy for a spe...

0 Sanjay Krishnan, et al. ∙

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