Niru Maheswaranathan

3.3k citations
18 papers · 610 · h-index 11

Impact in

    • Neural dynamics and brain function
    • Memory and Neural Mechanisms
    • Visual perception and processing mechanisms
    • Sleep and Wakefulness Research
    • Neuroscience and Neuropharmacology Research
    • Photoreceptor and optogenetics research
    • Neurobiology and Insect Physiology Research

Papers in

Niru Maheswaranathan

18 papers receiving 592 citations

Peers

Niru Maheswaranathan
Comparison fields: 5 of 85
  • Cognitive Neuroscience 382
  • Cellular and Molecular Neuroscience 248
  • Sensory Systems 48
  • Behavioral Neuroscience 20
  • Endocrine and Autonomic Systems 31
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Citations per year

Countries citing papers authored by Niru Maheswaranathan

Since Specialization
Citations

This map shows the geographic impact of Niru Maheswaranathan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Niru Maheswaranathan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Niru Maheswaranathan more than expected).

Fields of papers citing papers by Niru Maheswaranathan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Niru Maheswaranathan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Niru Maheswaranathan. The network helps show where Niru Maheswaranathan may publish in the future.

Co-authors

The 25 scholars most cited alongside Niru Maheswaranathan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Niru Maheswaranathan Line = papers co-authored together Niru Maheswaranathan links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 2017188
2 2017111
3
Deep Learning Models of the Retinal Response to Natural Scenes.
201679
4 201952
5 201749
6 201842
7 201218
8 202317
9 201813
10
Learning Unsupervised Learning Rules
201810
11
Guided Evolutionary Strategies: Escaping the curse of dimensionality in random search
201810
12 20189
13
Meta-Learning Update Rules for Unsupervised Representation Learning
20184
14
Learned optimizers that outperform SGD on wall-clock and validation loss
20183
15 20172
16 20081
17 20211
18
Learned optimizers that outperform SGD on wall-clock and test loss.
20181

About Niru Maheswaranathan

Niru Maheswaranathan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Cellular and Molecular Neuroscience and Molecular Biology, having authored 18 papers that have together received 610 indexed citations. Recurring topics across this work include Neural dynamics and brain function (5 papers), Advanced Neural Network Applications (4 papers), Domain Adaptation and Few-Shot Learning (4 papers), Machine Learning and Data Classification (3 papers), Machine Learning and Algorithms (2 papers), Stochastic Gradient Optimization Techniques (2 papers), Neurobiology and Insect Physiology Research (2 papers) and Retinal Development and Disorders (2 papers). The work is most often cited by research in Cognitive Neuroscience (382 citations), Cellular and Molecular Neuroscience (248 citations), Sensory Systems (48 citations), Behavioral Neuroscience (20 citations) and Endocrine and Autonomic Systems (31 citations). Niru Maheswaranathan has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Surya Ganguli, Kiah Hardcastle, Lisa M. Giocomo, Stephen A. Baccus, Jascha Sohl‐Dickstein, Lane McIntosh, Aran Nayebi, David B. Kastner, Luke Metz and Cindy F. Yang. Their work appears in journals such as Neuron, Review of Scientific Instruments, PLoS Computational Biology, Frontiers in Computational Neuroscience and International Conference on Learning Representations.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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