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Farhad Pourkamali-Anaraki

3 papers hereh-index 214 citations3 works total

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

author position
  • sole author1
  • first author1
  • last author1

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • stat.ML1
same name
  • Farhad Pourkamali-Anaraki — 3 papers, h 3
  • Farhad Pourkamali-Anaraki — 2 papers, h 2
  • Farhad Pourkamali-Anaraki — 1 paper, h 0
  • Farhad Pourkamali-Anaraki — 1 paper, h 5
  • Farhad Pourkamali-Anaraki — 1 paper, h 3

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
20192026
most citedThe Effectiveness of Variational Autoencoders for Active Learning

2 citations · 2 across the 2 of their papers we have counts for

collaborators

3 papers

stat.ML2026

Ensemble-Based Dirichlet Modeling for Predictive Uncertainty and Selective Classification

Courtney Franzen, Farhad Pourkamali-Anaraki

Neural network classifiers trained with cross-entropy loss achieve strong predictive accuracy but lack the capability to provide inherent predictive uncertainty estimates, thus req…

cs.LG2024

Kolmogorov-Arnold Networks in Low-Data Regimes: A Comparative Study with Multilayer Perceptrons

Farhad Pourkamali-Anaraki

Multilayer Perceptrons (MLPs) have long been a cornerstone in deep learning, known for their capacity to model complex relationships. Recently, Kolmogorov-Arnold Networks (KANs) ha…

cs.LG2019★ 2 cited

The Effectiveness of Variational Autoencoders for Active Learning

Farhad Pourkamali-Anaraki, Michael B. Wakin

The high cost of acquiring labels is one of the main challenges in deploying supervised machine learning algorithms. Active learning is a promising approach to control the learning…

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