298 citations · 692 across the 17 of their papers we have counts for
19 papers
DiRotQ: Rotation-Aware Quantization for 4-bit Diffusion Transformers
Sayeh Sharify, Mahsa Salmani, Hesham Mostafa
Diffusion Transformers (DiTs) achieve state-of-the-art image generation quality but incur substantial memory and computational costs at inference. While aggressive Post-Training Qu…
Early Attentive Sparsification Accelerates Neural Speech Transcription
Zifei Xu, Sayeh Sharify, Hesham Mostafa +3
Transformer-based neural speech processing has achieved state-of-the-art performance. Since speech audio signals are known to be highly compressible, here we seek to accelerate neu…
Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs
Shengyu Feng, Subarna Tripathi, Hesham Mostafa +2
Dynamic scene graph generation from a video is challenging due to the temporal dynamics of the scene and the inherent temporal fluctuations of predictions. We hypothesize that capt…
Implicit SVD for Graph Representation Learning
Sami Abu-El-Haija, Hesham Mostafa, Marcel Nassar +3
Recent improvements in the performance of state-of-the-art (SOTA) methods for Graph Representational Learning (GRL) have come at the cost of significant computational resource requ…
Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs
Hesham Mostafa
We present the Sequential Aggregation and Rematerialization (SAR) scheme for distributed full-batch training of Graph Neural Networks (GNNs) on large graphs. Large-scale training o…
On Local Aggregation in Heterophilic Graphs
Hesham Mostafa, Marcel Nassar, Somdeb Majumdar
Many recent works have studied the performance of Graph Neural Networks (GNNs) in the context of graph homophily - a label-dependent measure of connectivity. Traditional GNNs gener…