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researcher

S. Mohamed

32 papers hereh-index 3830.1k citations61 works total

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

author position
  • first author5
  • middle author17
  • last author10

Across the 32 of 32 papers where every author was matched, so the position is known.

fields
  • stat.ML14
  • cs.LG10
  • cs.AI2
  • cs.CL1
  • cs.CV1
  • cs.CY1
same name
  • S. Mohamed — 43 papers, h 9
  • S. Mohamed — 39 papers, h 19
  • S. Mohamed — 5 papers, h 2
  • S. Mohamed — 5 papers, h 4
  • S. Mohamed — 5 papers, h 10
  • S. Mohamed — 2 papers, h 17

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
20072023
most citedStochastic Backpropagation and Approximate Inference in Deep Generative Models

2.5k citations · 9k across the 32 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.LG2018★ 6 cited

Learning Implicit Generative Models with the Method of Learned Moments

Suman Ravuri, Shakir Mohamed, Mihaela Rosca +1

We propose a method of moments (MoM) algorithm for training large-scale implicit generative models. Moment estimation in this setting encounters two problems: it is often difficult…

cs.LG2018★ 129 cited

Implicit Reparameterization Gradients

Michael Figurnov, Shakir Mohamed, Andriy Mnih

By providing a simple and efficient way of computing low-variance gradients of continuous random variables, the reparameterization trick has become the technique of choice for trai…

cs.LG2018★ 141 cited

Unsupervised Predictive Memory in a Goal-Directed Agent

Greg Wayne, Chia-Chun Hung, David Amos +21

Animals execute goal-directed behaviours despite the limited range and scope of their sensors. To cope, they explore environments and store memories maintaining estimates of import…

stat.ML2018★ 47 cited

Distribution Matching in Variational Inference

Mihaela Rosca, Balaji Lakshminarayanan, Shakir Mohamed

With the increasingly widespread deployment of generative models, there is a mounting need for a deeper understanding of their behaviors and limitations. In this paper, we expose t…

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