3 code implementations • ICLR 2021 • Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato
Both uncertainty estimation and interpretability are important factors for trustworthy machine learning systems.
no code implementations • ICLR 2020 • Tameem Adel, Han Zhao, Richard E. Turner
Approaches to continual learning aim to successfully learn a set of related tasks that arrive in an online manner.
1 code implementation • ICLR 2020 • Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon
We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification setting.
no code implementations • NeurIPS Workshop Deep_Invers 2019 • Andreas Kattamis, Tameem Adel, Adrian Weller
Finally, we examine the adversarial invariancy of the early DIP outputs, and hypothesize that these outputs may remove non-robust image features.
no code implementations • ICLR 2019 • Tameem Adel, Cuong V. Nguyen, Richard E. Turner, Zoubin Ghahramani, Adrian Weller
We present a framework for interpretable continual learning (ICL).
no code implementations • ICML 2018 • Tameem Adel, Zoubin Ghahramani, Adrian Weller
We use a generative model which takes as input the representation in an existing (generative or discriminative) model, weakly supervised by limited side information.
1 code implementation • 15 Feb 2017 • Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling
This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input.
no code implementations • 27 Aug 2016 • Tameem Adel, Cassio P. de Campos
To the best of our knowledge, this is the first exact algorithm for this problem.
no code implementations • 28 Jun 2016 • Alexander Moreno, Tameem Adel, Edward Meeds, James M. Rehg, Max Welling
Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models.
no code implementations • 3 Aug 2015 • Tameem Adel, Alexander Wong, Daniel Stashuk
In this study, a spectral graph-theoretic grouping strategy for weakly supervised classification is introduced, where a limited number of labelled samples and a larger set of unlabelled samples are used to construct a larger annotated training set composed of strongly labelled and weakly labelled samples.
no code implementations • 26 Sep 2013 • Tameem Adel, Benn Smith, Ruth Urner, Daniel Stashuk, Daniel J. Lizotte
We present a comprehensive study of the use of generative modeling approaches for Multiple-Instance Learning (MIL) problems.