Search Results for author:
Found 8 papers, 2 papers with code
CODAH: An Adversarially Authored Question-Answer Dataset for Common Sense
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)
CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense
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.
Drive2Vec: Multiscale State-Space Embedding of Vehicular Sensor Data
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
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.
Experimental robustness of Fourier Ptychography phase retrieval algorithms
Both noise (e. g. Poisson noise) and model mis-match errors are shown to scale with intensity.
Data-Driven Design for Fourier Ptychographic Microscopy
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.
AI Deception: A Survey of Examples, Risks, and Potential Solutions
This paper argues that a range of current AI systems have learned how to deceive humans.
The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning
To measure these risks of malicious use, government institutions and major AI labs are developing evaluations for hazardous capabilities in LLMs.
