71 citations · 86 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 6 cited
Multi-scale Feature Learning Dynamics: Insights for Double Descent
Mohammad Pezeshki, Amartya Mitra, Yoshua Bengio +1
A key challenge in building theoretical foundations for deep learning is the complex optimization dynamics of neural networks, resulting from the high-dimensional interactions betw…
cs.CL2017★ 71 cited
Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks
Ying Zhang, Mohammad Pezeshki, Philemon Brakel +3
Convolutional Neural Networks (CNNs) are effective models for reducing spectral variations and modeling spectral correlations in acoustic features for automatic speech recognition…
cs.NE2015★ 9 cited
Sequence Modeling using Gated Recurrent Neural Networks
Mohammad Pezeshki
In this paper, we have used Recurrent Neural Networks to capture and model human motion data and generate motions by prediction of the next immediate data point at each time-step.…