Search Results for author: Abubakar Abid

Found 16 papers, 9 papers with code

Clustering Plotted Data by Image Segmentation

1 code implementation6 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.

Instance Segmentation Semantic Segmentation

Meaningfully Explaining Model Mistakes Using Conceptual Counterfactuals

1 code implementation24 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).

Persistent Anti-Muslim Bias in Large Language Models

1 code implementation14 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.

Adversarial Text Language Modelling +1

MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning

1 code implementation5 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.

Improving Training on Noisy Stuctured Labels

no code implementations8 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.

Semantic Segmentation

Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild

1 code implementation6 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.

Contrastive Variational Autoencoder Enhances Salient Features

1 code implementation12 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.

Contrastive Learning

Concrete Autoencoders for Differentiable Feature Selection and Reconstruction

2 code implementations27 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.

General Classification General Classification Selection

Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders

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.

Dynamic Time Warping Time Series

Contrastive Multivariate Singular Spectrum Analysis

no code implementations31 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.

Dimensionality Reduction Time Series

Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders

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.

Dynamic Time Warping Time Series

Stochastic EM for Shuffled Linear Regression

no code implementations2 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.

INTERPRETATION OF NEURAL NETWORK IS FRAGILE

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.

Feature Importance

Interpretation of Neural Networks is Fragile

1 code implementation29 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.

Feature Importance

Contrastive Principal Component Analysis

1 code implementation20 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.

Denoising

Linear Regression with Shuffled Labels

no code implementations3 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.

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