activity
20192022
most citedTowards calibrated and scalable uncertainty representations for neural networks

11 citations · 18 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20226 cited

Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data

Nabeel Seedat, Jonathan Crabbé, Ioana Bica +1

High model performance, on average, can hide that models may systematically underperform on subgroups of the data. We consider the tabular setting, which surfaces the unique issue…

cs.LG20201 cited

MCU-Net: A framework towards uncertainty representations for decision support system patient referrals in healthcare contexts

Nabeel Seedat

Incorporating a human-in-the-loop system when deploying automated decision support is critical in healthcare contexts to create trust, as well as provide reliable performance on a…

cs.CV2020

Automated machine vision enabled detection of movement disorders from hand drawn spirals

Nabeel Seedat, Vered Aharonson, Ilana Schlesinger

A widely used test for the diagnosis of Parkinson's disease (PD) and Essential tremor (ET) is hand-drawn shapes,where the analysis is observationally performed by the examining neu…

cs.LG2020

Machine learning discrimination of Parkinson's Disease stages from walker-mounted sensors data

Nabeel Seedat, Vered Aharonson

Clinical methods that assess gait in Parkinson's Disease (PD) are mostly qualitative. Quantitative methods necessitate costly instrumentation or cumbersome wearable devices, which…

cs.LG201911 cited

Towards calibrated and scalable uncertainty representations for neural networks

Nabeel Seedat, Christopher Kanan

For many applications it is critical to know the uncertainty of a neural network's predictions. While a variety of neural network parameter estimation methods have been proposed fo…