no code implementations • 26 May 2023 • Erik C. Johnson, Brian S. Robinson, Gautam K. Vallabha, Justin Joyce, Jordan K. Matelsky, Raphael Norman-Tenazas, Isaac Western, Marisel Villafañe-Delgado, Martha Cervantes, Michael S. Robinette, Arun V. Reddy, Lindsey Kitchell, Patricia K. Rivlin, Elizabeth P. Reilly, Nathan Drenkow, Matthew J. Roos, I-Jeng Wang, Brock A. Wester, William R. Gray-Roncal, Joan A. Hoffmann
We envision a pipeline to utilize large neuroimaging datasets, including maps of the brain which capture neuron and synapse connectivity, to improve machine learning approaches.
1 code implementation • 19 Aug 2022 • Jared Markowitz, Ryan W. Gardner, Ashley Llorens, Raman Arora, I-Jeng Wang
Without cost constraints, we find that pessimistic risk profiles can be used to reduce cost while improving total reward accumulation.
no code implementations • 29 Sep 2021 • Jared Markowitz, Ryan Gardner, Ashley Llorens, Raman Arora, I-Jeng Wang
Standard deep reinforcement learning (DRL) agents aim to maximize expected reward, considering collected experiences equally in formulating a policy.
no code implementations • 22 Dec 2020 • Kapil Katyal, Yuxiang Gao, Jared Markowitz, Sara Pohland, Corban Rivera, I-Jeng Wang, Chien-Ming Huang
Human-aware robot navigation promises a range of applications in which mobile robots bring versatile assistance to people in common human environments.
no code implementations • 1 May 2020 • Neil Fendley, Max Lennon, I-Jeng Wang, Philippe Burlina, Nathan Drenkow
We focus on the development of effective adversarial patch attacks and -- for the first time -- jointly address the antagonistic objectives of attack success and obtrusiveness via the design of novel semi-transparent patches.
no code implementations • 25 Feb 2020 • William Paul, I-Jeng Wang, Fady Alajaji, Philippe Burlina
Our work focuses on unsupervised and generative methods that address the following goals: (a) learning unsupervised generative representations that discover latent factors controlling image semantic attributes, (b) studying how this ability to control attributes formally relates to the issue of latent factor disentanglement, clarifying related but dissimilar concepts that had been confounded in the past, and (c) developing anomaly detection methods that leverage representations learned in (a).
1 code implementation • 25 Feb 2020 • Corban G. Rivera, Olivia Lyons, Arielle Summitt, Ayman Fatima, Ji Pak, William Shao, Robert Chalmers, Aryeh Englander, Edward W. Staley, I-Jeng Wang, Ashley J. Llorens
In this work, we introduce the AI safety TanksWorld as an environment for AI safety research with three essential aspects: competing performance objectives, human-machine teaming, and multi-agent competition.
no code implementations • 28 May 2018 • Wojciech Czaja, Neil Fendley, Michael Pekala, Christopher Ratto, I-Jeng Wang
This paper considers attacks against machine learning algorithms used in remote sensing applications, a domain that presents a suite of challenges that are not fully addressed by current research focused on natural image data such as ImageNet.
no code implementations • 8 Dec 2017 • Jared Markowitz, Aurora C. Schmidt, Philippe M. Burlina, I-Jeng Wang
We address zero-shot (ZS) learning, building upon prior work in hierarchical classification by combining it with approaches based on semantic attribute estimation.
no code implementations • 24 Jun 2014 • Nam H. Lee, I-Jeng Wang, Youngser Park, Care E. Priebe, Michael Rosen
We consider a problem of grouping multiple graphs into several clusters using singular value thesholding and non-negative factorization.
no code implementations • 24 Jun 2014 • Nam H. Lee, Carey Priebe, Youngser Park, I-Jeng Wang, Michael Rosen
A natural approach to analyze interaction data of form "what-connects-to-what-when" is to create a time-series (or rather a sequence) of graphs through temporal discretization (bandwidth selection) and spatial discretization (vertex contraction).