2 citations · 4 across the 17 of their papers we have counts for
5 papers · 1 filter
GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values
Farnoosh Javadi, Walid Ahmed, Habib Hajimolahoseini +7
Massive transformer-based models face several challenges, including slow and computationally intensive pre-training and over-parametrization. This paper addresses these challenges…
SwiftLearn: A Data-Efficient Training Method of Deep Learning Models using Importance Sampling
Habib Hajimolahoseini, Omar Mohamed Awad, Walid Ahmed +8
In this paper, we present SwiftLearn, a data-efficient approach to accelerate training of deep learning models using a subset of data samples selected during the warm-up stages of…
Speeding up Resnet Architecture with Layers Targeted Low Rank Decomposition
Walid Ahmed, Habib Hajimolahoseini, Austin Wen +1
Compression of a neural network can help in speeding up both the training and the inference of the network. In this research, we study applying compression using low rank decomposi…
Improving Resnet-9 Generalization Trained on Small Datasets
Omar Mohamed Awad, Habib Hajimolahoseini, Michael Lim +4
This paper presents our proposed approach that won the first prize at the ICLR competition on Hardware Aware Efficient Training. The challenge is to achieve the highest possible ac…
Training Acceleration of Low-Rank Decomposed Networks using Sequential Freezing and Rank Quantization
Habib Hajimolahoseini, Walid Ahmed, Yang Liu
Low Rank Decomposition (LRD) is a model compression technique applied to the weight tensors of deep learning models in order to reduce the number of trainable parameters and comput…