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researcher

S. Razavi

4 papers hereh-index 6108 citations15 works total

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

author position
  • first author3
  • middle author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • eess.AS1
same name
  • S. Razavi — 3 papers, h 16
  • S. Razavi — 3 papers, h 24
  • S. Razavi — 2 papers, h 8
  • S. Razavi — 2 papers, h 9
  • S. Razavi — 2 papers, h 2
  • S. Razavi — 1 paper, h 4

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
20202023
most citedParsiNorm: A Persian Toolkit for Speech Processing Normalization

1 citations · 1 across the 3 of their papers we have counts for

collaborators

4 papers

cs.LG2023

Out-of-distribution detection using normalizing flows on the data manifold

Seyedeh Fatemeh Razavi, Mohammad Mahdi Mehmanchi, Reshad Hosseini +1

Using the intuition that out-of-distribution data have lower likelihoods, a common approach for out-of-distribution detection involves estimating the underlying data distribution.…

cs.LG2022

Joint Manifold Learning and Density Estimation Using Normalizing Flows

Seyedeh Fatemeh Razavi, Mohammad Mahdi Mehmanchi, Reshad Hosseini +1

Based on the manifold hypothesis, real-world data often lie on a low-dimensional manifold, while normalizing flows as a likelihood-based generative model are incapable of finding t…

eess.AS2021★ 1 cited

ParsiNorm: A Persian Toolkit for Speech Processing Normalization

Romina Oji, Seyedeh Fatemeh Razavi, Sajjad Abdi Dehsorkh +3

In general, speech processing models consist of a language model along with an acoustic model. Regardless of the language model's complexity and variants, three critical pre-proces…

cs.LG2020

FRMDN: Flow-based Recurrent Mixture Density Network

Seyedeh Fatemeh Razavi, Reshad Hosseini, Tina Behzad

The class of recurrent mixture density networks is an important class of probabilistic models used extensively in sequence modeling and sequence-to-sequence mapping applications. I…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.