no code implementations • 31 Oct 2022 • Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, Astro Teller
In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society.
no code implementations • 3 Feb 2022 • Molly O'Brien, Julia Bukowski, Mathias Unberath, Aria Pezeshk, Greg Hager
Understanding Deep Neural Network (DNN) performance in changing conditions is essential for deploying DNNs in safety critical applications with unconstrained environments, e. g., perception for self-driving vehicles or medical image analysis.
no code implementations • 17 Aug 2021 • Molly O'Brien, Mike Medoff, Julia Bukowski, Greg Hager
We propose the task Network Generalization Prediction: predicting the expected network performance in novel operating domains.
no code implementations • 27 Apr 2020 • Polina Golland, Jack Gallant, Greg Hager, Hanspeter Pfister, Christos Papadimitriou, Stefan Schaal, Joshua T. Vogelstein
In December 2014, a two-day workshop supported by the Computing Community Consortium (CCC) and the National Science Foundation's Computer and Information Science and Engineering Directorate (NSF CISE) was convened in Washington, DC, with the goal of bringing together computer scientists and brain researchers to explore these new opportunities and connections, and develop a new, modern dialogue between the two research communities.
no code implementations • 20 Dec 2019 • Molly O'Brien, William Goble, Greg Hager, Julia Bukowski
Our results demonstrate that we can accurately predict the ML Dependability, Task Undependability, and Harmful Undependability for operating conditions that are significantly different from the testing conditions.
no code implementations • 27 Mar 2013 • Greg Hager, Hugh F. Durrant-Whyte
We consider the sensors of a multi-sensor system to be members or agents of a team, able to offer opinions and bargain in group decisions.
no code implementations • 27 Mar 2013 • Greg Hager, Max Mintz
In this paper, we evaluate three estimation techniques: the extended Kalman filter, a discrete Bayes approximation, and an iterative Bayes approximation.