Narsis Kiani
researcher
Facts
9 properties- Instance of
- human
- Date of birth
- September 21, 1978
- Place of birth
- Iran
- Occupation
- mathematician
- Employer
- Karolinska Institutet[4]
- Educated at
- Islamic Azad University
- Doctoral advisor
- Tofigh Allahviranloo, Mehdi Sadeghi, Changiz Eslahchi[5][6][7]
- Maintained by WikiProject
- WikiProject Mathematics[9]
🏷️ Also known as
en Narsis Aftab Kiani
en Narsis Kiani
🔖 Identifiers · 13
- ISNI
- 0000000505908752
- ORCID iD
- 0000-0002-0949-046X
- VIAF cluster ID
- 205162902522777780357
- GND ID
- 1161016198
- DBLP author ID
- 116/4558
- DDB person (GND) ID
- 1161016198
- Google Knowledge Graph ID
- /g/11gbjmh4_b
- Google Scholar author ID
- odJXANUAAAAJ
- MR Author ID
- 806496
- Mathematics Genealogy Project ID
- 157039
- Scopus author ID
- 24492005900
- Yale LUX ID
- person/99b6bc51-d7bf-4a8b-b3ae-ddcda46d13f1
Show all 13 identifiers +1
- ZbMATH author ID
- kiani.narsis-aftab
🔗 Connections
Author of 7
Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity,
Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces,
A Parameter-Efficient Learning Approach to Arabic Dialect Identification with Pre-Trained General-Purpose Speech Model,
Assembly Theory is an approximation to algorithmic complexity based on LZ compression that does not explain selection or evolution,
Causal deconvolution by algorithmic generative models,
Reusability report: Learning the transcriptional grammar in single-cell RNA-sequencing data using transformers,
Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition
← Authored by 7
Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity,
Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces,
A Parameter-Efficient Learning Approach to Arabic Dialect Identification with Pre-Trained General-Purpose Speech Model,
Assembly Theory is an approximation to algorithmic complexity based on LZ compression that does not explain selection or evolution,
Causal deconvolution by algorithmic generative models,
Reusability report: Learning the transcriptional grammar in single-cell RNA-sequencing data using transformers,
Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition
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