activity
20182022
most citedImportance of Data Loading Pipeline in Training Deep Neural Networks

11 citations · 26 across the 9 of their papers we have counts for

collaborators

19 papers

cs.CV20221 cited

SeKron: A Decomposition Method Supporting Many Factorization Structures

Marawan Gamal Abdel Hameed, Ali Mosleh, Marzieh S. Tahaei +1

While convolutional neural networks (CNNs) have become the de facto standard for most image processing and computer vision applications, their deployment on edge devices remains ch…

cs.LG2021

Demystifying and Generalizing BinaryConnect

Tim Dockhorn, Yaoliang Yu, Eyyüb Sari +2

BinaryConnect (BC) and its many variations have become the de facto standard for neural network quantization. However, our understanding of the inner workings of BC is still quite…

cs.CL2021

Kronecker Decomposition for GPT Compression

Ali Edalati, Marzieh Tahaei, Ahmad Rashid +3

GPT is an auto-regressive Transformer-based pre-trained language model which has attracted a lot of attention in the natural language processing (NLP) domain due to its state-of-th…

cs.CL202110 cited

KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation

Marzieh S. Tahaei, Ella Charlaix, Vahid Partovi Nia +2

The development of over-parameterized pre-trained language models has made a significant contribution toward the success of natural language processing. While over-parameterization…

stat.ML2021

A Twin Neural Model for Uplift

Mouloud Belbahri, Olivier Gandouet, Alejandro Murua +1

Uplift is a particular case of conditional treatment effect modeling. Such models deal with cause-and-effect inference for a specific factor, such as a marketing intervention or a…

cs.LG20201 cited

Tensor train decompositions on recurrent networks

Alejandro Murua, Ramchalam Ramakrishnan, Xinlin Li +2

Recurrent neural networks (RNN) such as long-short-term memory (LSTM) networks are essential in a multitude of daily live tasks such as speech, language, video, and multimodal lear…