Marius Pachitariu
Artificial Intelligence · 2013–2024
A researcher in the Science corpus: 13 papers; trusts 244 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 73 citations into it; #14,200 of 35,345 by trust here, #22,463 by citations
- Cognitive Neuroscience — 73 citations into it
- Computer Vision and Pattern Recognition — 65 citations into it
- Cellular and Molecular Neuroscience — 57 citations into it
- Biophysics — 46 citations into it; #740 of 1,196 by trust here, #510 by citations
- Media Technology — 26 citations into it; #2,997 of 3,621 by trust here, #1,111 by citations
- Molecular Biology — 19 citations into it; #14,425 of 17,773 by trust here, #12,742 by citations
- Evaluation & Benchmarks — 18 citations into it
Whom Marius Pachitariu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Ilya Sutskever
- Andriy Mnih
- Yoshua Bengio
- Alex Krizhevsky
- Sepp Hochreiter
- Tomas Mikolov
- Kaiming He
- Yann LeCun
- Diederik P. Kingma
Who trusts Marius Pachitariu overall
- Yarin Gal
- C. Stringer
- Rohollah Soltani
- Behnam Neyshabur
- Nicholas A. Steinmetz
- Justin Bayer
- Mengsen Zhang
- Zoubin Ghahramani
- Eftychios A. Pnevmatikakis
- Hui Jiang
Papers from Marius Pachitariu's perspective
Ordered by how strongly the researchers Marius Pachitariu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Regularization and nonlinearities for neural language models: when are they needed? (2013) — 47 citations
- Cellpose: a generalist algorithm for cellular segmentation (2020) — 3,569 citations
- Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings (2020) — 1,064 citations
- Cellpose 2.0: how to train your own model (2022) — 1,118 citations
- Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels (2016) — 668 citations
- Spike sorting with Kilosort4 (2024) — 320 citations
- Cellpose3: one-click image restoration for improved cellular segmentation (2024) — 377 citations
- Survey of spiking in the mouse visual system reveals functional hierarchy (2021)
- Spontaneous Behaviors Drive Multidimensional, Brain-wide Activity (2019) — 902 citations
- High-dimensional geometry of population responses in visual cortex (2018) — 647 citations
- Fully Integrated Silicon Probes for High-Density Recording of Neural Activity (2017) — 2,007 citations
- Fast and accurate spike sorting of high-channel count probes with KiloSort (2016) — 451 citations
- Suite2p: beyond 10,000 neurons with standard two-photon microscopy (2016) — 1,087 citations