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researcher

A. Kannan

12 papers hereh-index 273.8k citations74 works total

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

author position
  • first author2
  • middle author9
  • last author1

Across the 12 of 12 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.AI2
  • cs.CV2
  • cs.IR2
  • cs.CL1
  • stat.ML1
same name
  • A. Kannan — 6 papers, h 10
  • A. Kannan — 3 papers, h 4
  • A. Kannan — 2 papers, h 15
  • A. Kannan — 1 paper, h 2
  • A. Kannan — 1 paper
  • A. Kannan — 1 paper, h 8

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
20122020
most citedBest of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model

85 citations · 148 across the 8 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2019★ 2 cited

The accuracy vs. coverage trade-off in patient-facing diagnosis models

Anitha Kannan, Jason Alan Fries, Eric Kramer +3

A third of adults in America use the Internet to diagnose medical concerns, and online symptom checkers are increasingly part of this process. These tools are powered by diagnosis…

cs.LG2019★ 5 cited

Domain-Relevant Embeddings for Medical Question Similarity

Clara McCreery, Namit Katariya, Anitha Kannan +2

The rate at which medical questions are asked online far exceeds the capacity of qualified people to answer them, and many of these questions are not unique. Identifying same-quest…

cs.LG2019★ 10 cited

Open Set Medical Diagnosis

Viraj Prabhu, Anitha Kannan, Geoffrey J. Tso +4

Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i.e. that models will only encounter conditions on which they ha…

cs.LG2017★ 42 cited

Tackling Over-pruning in Variational Autoencoders

Serena Yeung, Anitha Kannan, Yann Dauphin +1

Variational autoencoders (VAE) are directed generative models that learn factorial latent variables. As noted by Burda et al. (2015), these models exhibit the problem of factor ove…

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