87 citations · 117 across the 3 of their papers we have counts for
4 papers
Improving Multimodal Accuracy Through Modality Pre-training and Attention
Aya Abdelsalam Ismail, Mahmudul Hasan, Faisal Ishtiaq
Training a multimodal network is challenging and it requires complex architectures to achieve reasonable performance. We show that one reason for this phenomena is the difference b…
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…
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…