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Faruk Ahmed

6 papers hereh-index 712k citations12 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author4

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CV2
same name
  • Faruk Ahmed — 6 papers, h 5
  • Faruk Ahmed — 3 papers, h 4
  • Faruk Ahmed — 3 papers, h 2
  • Faruk Ahmed — 2 papers, h 1
  • Faruk Ahmed — 1 paper, h 7
  • Faruk Ahmed — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20142020
most citedImproved Training of Wasserstein GANs

1.5k citations · 1.6k across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2020★ 2 cited

Integrating Categorical Semantics into Unsupervised Domain Translation

Samuel Lavoie, Faruk Ahmed, Aaron Courville

While unsupervised domain translation (UDT) has seen a lot of success recently, we argue that mediating its translation via categorical semantic features could broaden its applicab…

cs.LG2017★ 1.5k cited

Improved Training of Wasserstein GANs

Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky +2

Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stab…

cs.LG2016★ 81 cited

PixelVAE: A Latent Variable Model for Natural Images

Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed +4

Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and model global structure well but ha…

cs.LG2015★ 78 cited

Reducing Overfitting in Deep Networks by Decorrelating Representations

Michael Cogswell, Faruk Ahmed, Ross Girshick +2

One major challenge in training Deep Neural Networks is preventing overfitting. Many techniques such as data augmentation and novel regularizers such as Dropout have been proposed…

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