565 citations · 1.2k across the 20 of their papers we have counts for
9 papers · 1 filter
STG-MTL: Scalable Task Grouping for Multi-Task Learning Using Data Map
Ammar Sherif, Abubakar Abid, Mustafa Elattar +1
Multi-Task Learning (MTL) is a powerful technique that has gained popularity due to its performance improvement over traditional Single-Task Learning (STL). However, MTL is often c…
Clustering Plotted Data by Image Segmentation
Tarek Naous, Srinjay Sarkar, Abubakar Abid +1
Clustering algorithms are one of the main analytical methods to detect patterns in unlabeled data. Existing clustering methods typically treat samples in a dataset as points in a m…
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Alexandros Karargyris, Renato Umeton, Micah J. Sheller +39
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving prov…
Meaningfully Debugging Model Mistakes using Conceptual Counterfactual Explanations
Abubakar Abid, Mert Yuksekgonul, James Zou
Understanding and explaining the mistakes made by trained models is critical to many machine learning objectives, such as improving robustness, addressing concept drift, and mitiga…
Improving Training on Noisy Stuctured Labels
Abubakar Abid, James Zou
Fine-grained annotations---e.g. dense image labels, image segmentation and text tagging---are useful in many ML applications but they are labor-intensive to generate. Moreover ther…
Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild
Abubakar Abid, Ali Abdalla, Ali Abid +3
Accessibility is a major challenge of machine learning (ML). Typical ML models are built by specialists and require specialized hardware/software as well as ML experience to valida…
Contrastive Variational Autoencoder Enhances Salient Features
Abubakar Abid, James Zou
Variational autoencoders are powerful algorithms for identifying dominant latent structure in a single dataset. In many applications, however, we are interested in modeling latent…
Concrete Autoencoders for Differentiable Feature Selection and Reconstruction
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…
Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders
Abubakar Abid, James Zou
Measuring similarities between unlabeled time series trajectories is an important problem in domains as diverse as medicine, astronomy, finance, and computer vision. It is often un…