1 code implementation • 6 Oct 2021 • Tarek Naous, Srinjay Sarkar, Abubakar Abid, James Zou
We describe the method and compare it to ten other clustering methods on synthetic data to illustrate its advantages and disadvantages.
1 code implementation • 24 Jun 2021 • Abubakar Abid, Mert Yuksekgonul, James Zou
In this paper, we propose a systematic approach, conceptual counterfactual explanations(CCE), that explains why a classifier makes a mistake on a particular test sample(s) in terms of human-understandable concepts (e. g. this zebra is misclassified as a dog because of faint stripes).
1 code implementation • 14 Jan 2021 • Abubakar Abid, Maheen Farooqi, James Zou
It has been observed that large-scale language models capture undesirable societal biases, e. g. relating to race and gender; yet religious bias has been relatively unexplored.
1 code implementation • 5 Oct 2020 • Kexin Huang, Tianfan Fu, Dawood Khan, Ali Abid, Ali Abdalla, Abubakar Abid, Lucas M. Glass, Marinka Zitnik, Cao Xiao, Jimeng Sun
The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics.
no code implementations • 8 Mar 2020 • Abubakar Abid, James Zou
Systematic experiments on image segmentation and text tagging demonstrate the strong performance of ECN in improving training on noisy structured labels.
1 code implementation • 6 Jun 2019 • Abubakar Abid, Ali Abdalla, Ali Abid, Dawood Khan, Abdulrahman Alfozan, James Zou
Their feedback identified that Gradio should support a variety of interfaces and frameworks, allow for easy sharing of the interface, allow for input manipulation and interactive inference by the domain expert, as well as allow embedding the interface in iPython notebooks.
1 code implementation • 12 Feb 2019 • Abubakar Abid, James Zou
The cVAE explicitly models latent features that are shared between the datasets, as well as those that are enriched in one dataset relative to the other, which allows the algorithm to isolate and enhance the salient latent features.
2 code implementations • 27 Jan 2019 • Abubakar Abid, Muhammad Fatih Balin, James Zou
We introduce the concrete autoencoder, an end-to-end differentiable method for global feature selection, which efficiently identifies a subset of the most informative features and simultaneously learns a neural network to reconstruct the input data from the selected features.
Ranked #1 on
General Classification
on MNIST
no code implementations • NeurIPS 2018 • Abubakar Abid, James Y. Zou
We define a flexible and differentiable family of warping metrics, which encompasses common metrics such as DTW, Edit Distance, Euclidean, etc.
no code implementations • 31 Oct 2018 • Abdi-Hakin Dirie, Abubakar Abid, James Zou
We introduce Contrastive Multivariate Singular Spectrum Analysis, a novel unsupervised method for dimensionality reduction and signal decomposition of time series data.
no code implementations • NeurIPS 2018 • Abubakar Abid, James Zou
We define a flexible and differentiable family of warping metrics, which encompasses common metrics such as DTW, Euclidean, and edit distance.
no code implementations • 2 Apr 2018 • Abubakar Abid, James Zou
We consider the problem of inference in a linear regression model in which the relative ordering of the input features and output labels is not known.
no code implementations • ICLR 2018 • Amirata Ghorbani, Abubakar Abid, James Zou
In this paper, we show that interpretation of deep learning predictions is extremely fragile in the following sense: two perceptively indistinguishable inputs with the same predicted label can be assigned very different}interpretations.
1 code implementation • 29 Oct 2017 • Amirata Ghorbani, Abubakar Abid, James Zou
In this paper, we show that interpretation of deep learning predictions is extremely fragile in the following sense: two perceptively indistinguishable inputs with the same predicted label can be assigned very different interpretations.
1 code implementation • 20 Sep 2017 • Abubakar Abid, Martin J. Zhang, Vivek K. Bagaria, James Zou
We present a new technique called contrastive principal component analysis (cPCA) that is designed to discover low-dimensional structure that is unique to a dataset, or enriched in one dataset relative to other data.
no code implementations • 3 May 2017 • Abubakar Abid, Ada Poon, James Zou
We study the regimes in which each estimator excels, and generalize the estimators to the setting where partial ordering information is available in the form of experiments replicated independently.