87 citations · 137 across the 6 of their papers we have counts for
6 papers
Improving Deep Learning Interpretability by Saliency Guided Training
Aya Abdelsalam Ismail, Héctor Corrada Bravo, Soheil Feizi
Saliency methods have been widely used to highlight important input features in model predictions. Most existing methods use backpropagation on a modified gradient function to gene…
Benchmarking Deep Learning Interpretability in Time Series Predictions
Aya Abdelsalam Ismail, Mohamed Gunady, Héctor Corrada Bravo +1
Saliency methods are used extensively to highlight the importance of input features in model predictions. These methods are mostly used in vision and language tasks, and their appl…
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young +93
We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international…
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks
Aya Abdelsalam Ismail, Mohamed Gunady, Luiz Pessoa +2
Recent efforts to improve the interpretability of deep neural networks use saliency to characterize the importance of input features to predictions made by models. Work on interpre…
Improving Long-Horizon Forecasts with Expectation-Biased LSTM Networks
Aya Abdelsalam Ismail, Timothy Wood, Héctor Corrada Bravo
State-of-the-art forecasting methods using Recurrent Neural Net- works (RNN) based on Long-Short Term Memory (LSTM) cells have shown exceptional performance targeting short-horizon…
Anomaly Classification with the Anti-Profile Support Vector Machine
Wikum Dinalankara, Hector Corrada Bravo
We introduce the anti-profile Support Vector Machine (apSVM) as a novel algorithm to address the anomaly classification problem, an extension of anomaly detection where the goal is…