no code implementations • 5 Dec 2023 • Arun Reddy, William Paul, Corban Rivera, Ketul Shah, Celso M. de Melo, Rama Chellappa
In this work, we tackle the problem of unsupervised domain adaptation (UDA) for video action recognition.
no code implementations • 28 Sep 2023 • Qiao Gu, Alihusein Kuwajerwala, Sacha Morin, Krishna Murthy Jatavallabhula, Bipasha Sen, Aditya Agarwal, Corban Rivera, William Paul, Kirsty Ellis, Rama Chellappa, Chuang Gan, Celso Miguel de Melo, Joshua B. Tenenbaum, Antonio Torralba, Florian Shkurti, Liam Paull
We demonstrate the utility of this representation through a number of downstream planning tasks that are specified through abstract (language) prompts and require complex reasoning over spatial and semantic concepts.
1 code implementation • 17 Mar 2023 • Arun V. Reddy, Ketul Shah, William Paul, Rohita Mocharla, Judy Hoffman, Kapil D. Katyal, Dinesh Manocha, Celso M. de Melo, Rama Chellappa
The dataset is composed of both real and synthetic videos from seven gesture classes, and is intended to support the study of synthetic-to-real domain shift for video-based action recognition.
no code implementations • 15 Feb 2023 • William Paul, Philip Mathew, Fady Alajaji, Philippe Burlina
This paper investigates to what degree and magnitude tradeoffs exist between utility, fairness and attribute privacy in computer vision.
1 code implementation • 20 Jun 2022 • Adam Gronowski, William Paul, Fady Alajaji, Bahman Gharesifard, Philippe Burlina
Designing machine learning algorithms that are accurate yet fair, not discriminating based on any sensitive attribute, is of paramount importance for society to accept AI for critical applications.
no code implementations • 9 Mar 2022 • Adam Gronowski, William Paul, Fady Alajaji, Bahman Gharesifard, Philippe Burlina
We develop a novel method for ensuring fairness in machine learning which we term as the Renyi Fair Information Bottleneck (RFIB).
no code implementations • 3 Mar 2022 • William Paul, Philippe Burlina
We tackle here a specific, still not widely addressed aspect, of AI robustness, which consists of seeking invariance / insensitivity of model performance to hidden factors of variations in the data.
no code implementations • 28 Feb 2022 • Haolin Yuan, Armin Hadzic, William Paul, Daniella Villegas de Flores, Philip Mathew, John Aucott, Yinzhi Cao, Philippe Burlina
Skin lesions can be an early indicator of a wide range of infectious and other diseases.
no code implementations • 28 Jul 2021 • William Paul, Philippe Burlina
We also demonstrate how adaptation to real factors of variations can be performed in the semi-supervised case where some target factor labels are known, via automated intervention selection.
no code implementations • 4 Mar 2021 • William Paul, Yinzhi Cao, Miaomiao Zhang, Phil Burlina
Machine learning (ML) models used in medical imaging diagnostics can be vulnerable to a variety of privacy attacks, including membership inference attacks, that lead to violations of regulations governing the use of medical data and threaten to compromise their effective deployment in the clinic.
no code implementations • 11 Dec 2020 • William Paul, Armin Hadzic, Neil Joshi, Fady Alajaji, Phil Burlina
Our experiments also demonstrate the ability of these novel metrics in assessing the Pareto efficiency of the proposed methods.
no code implementations • 28 Sep 2020 • Philippe M. Burlina, William Paul, Phil A. Mathew, Neil J. Joshi, Alison W. Rebman, John N. Aucott
We examine progress in the use of AI for detecting skin lesions, with particular emphasis on the erythema migrans rash of acute Lyme disease, and other lesions, such as those from conditions like herpes zoster (shingles), tinea corporis, erythema multiforme, cellulitis, insect bites, or tick bites.
no code implementations • 3 Jun 2020 • Himesh Bhatia, William Paul, Fady Alajaji, Bahman Gharesifard, Philippe Burlina
Another novel GAN generator loss function is next proposed in terms of R\'{e}nyi cross-entropy functionals with order $\alpha >0$, $\alpha\neq 1$.
no code implementations • 28 Apr 2020 • Philippe Burlina, Neil Joshi, William Paul, Katia D. Pacheco, Neil M. Bressler
Using novel generative methods for addressing missing subpopulation training data (DR-referable darker-skin) achieved instead accuracy, for lighter-skin, of 72. 0% (65. 8%, 78. 2%), and for darker-skin, of 71. 5% (65. 2%, 77. 8%), demonstrating closer parity (delta=0. 5%) in accuracy across subpopulations (Welch t-test t=0. 111, P=. 912).
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 • 29 Dec 2019 • Roy Fox, Richard Shin, William Paul, Yitian Zou, Dawn Song, Ken Goldberg, Pieter Abbeel, Ion Stoica
Autonomous agents can learn by imitating teacher demonstrations of the intended behavior.
4 code implementations • 16 Dec 2017 • Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael. I. Jordan, Ion Stoica
To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system's control state.
2 code implementations • 11 Mar 2017 • Robert Nishihara, Philipp Moritz, Stephanie Wang, Alexey Tumanov, William Paul, Johann Schleier-Smith, Richard Liaw, Mehrdad Niknami, Michael. I. Jordan, Ion Stoica
Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making.