Search Results for author: Michael Chen

Found 6 papers, 2 papers with code

CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense

1 code implementation WS 2019 Michael Chen, Mike D{'}Arcy, Alisa Liu, Fern, Jared ez, Doug Downey

To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems.

Common Sense Reasoning Question Answering +1

CODAH: An Adversarially Authored Question-Answer Dataset for Common Sense

1 code implementation8 Apr 2019 Michael Chen, Mike D'Arcy, Alisa Liu, Jared Fernandez, Doug Downey

To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems.

 Ranked #1 on Common Sense Reasoning on CODAH (using extra training data)

Common Sense Reasoning Question Answering +1

Data-Driven Design for Fourier Ptychographic Microscopy

no code implementations8 Apr 2019 Michael Kellman, Emrah Bostan, Michael Chen, Laura Waller

In this work, we learn LED source pattern designs that compress the many required measurements into only a few, with negligible loss in reconstruction quality or resolution.

Experimental Design Super-Resolution

Drive2Vec: Multiscale State-Space Embedding of Vehicular Sensor Data

no code implementations12 Jun 2018 David Hallac, Suvrat Bhooshan, Michael Chen, Kacem Abida, Rok Sosic, Jure Leskovec

With automobiles becoming increasingly reliant on sensors to perform various driving tasks, it is important to encode the relevant CAN bus sensor data in a way that captures the general state of the vehicle in a compact form.

Best arm identification in multi-armed bandits with delayed feedback

no code implementations29 Mar 2018 Aditya Grover, Todor Markov, Peter Attia, Norman Jin, Nicholas Perkins, Bryan Cheong, Michael Chen, Zi Yang, Stephen Harris, William Chueh, Stefano Ermon

We propose a generalization of the best arm identification problem in stochastic multi-armed bandits (MAB) to the setting where every pull of an arm is associated with delayed feedback.

Hyperparameter Optimization Multi-Armed Bandits

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