Utkarsh Singhal

I am a PhD student at Berkeley AI Research Lab, which is a part of the EECS department at University of California, Berkeley. I work on computer vision, advised by Professors Stella Yu and Ren Ng.

Email  |  GitHub  |  Google Scholar   

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I'm interested in building more robust and adaptable deep learning models by taking inspiration from the human brain. My research interests span computer vision, meta-learning, optimization, and robotics.

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How to guess a gradient

Utkarsh Singhal, Brian Cheung, Kartik Chandra, Jonathan Ragan-Kelley, Joshua Tenenbaum, Tomaso Poggio, Stella Yu
Optimization for Machine Learning Workshop (OPT2023), NeurIPS, 2023

We use architecture and activations to guess a neural network’s gradients without computing the loss or using backprop.

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Learning to Transform for Generalizable Instance-wise Invariance

Utkarsh Singhal, Carlos Esteves, Ameesh Makadia, Stella Yu
International Conference on Computer Vision (ICCV), 2023
paper | code | website

We predict a distribution of transformations for any input image. This can be used for data augmentation, aligning instances, and adapting to out-of-distribution poses.

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Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models

Utkarsh Singhal, Stella X Yu, Zackery Steck, Scott Kangas, Aaron A Reite
(Oral) Humanitarian Aid and Disaster Workshop (HADR), NeurIPS, 2022

We apply our CDS (co-domain symmetry) work on a small and imbalanced dataset in the MSI classification setting.

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Co-domain symmetry for complex-valued deep learning

Utkarsh Singhal, Yifei Xing, Stella Yu
Computer Vision and Pattern Recognition (CVPR), 2022
paper | code

We make complex-valued CNNs that are invariant to scale and phase-shifts of the input pixels, and apply it to SAR image classification.

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Fourier features let networks learn high frequency functions in low dimensional domains

Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, Ren Ng
(Spotlight) NeurIPS, 2021
paper | code | website

We explain why neural networks fail to learn low-dimensional functions and how position encoding (fourier features) help.

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LO represents motion and semantic categories in addition to object boundaries

Utkarsh Singhal, Jack Gallant, Mark Lescroart
Journal of Vision, 2019

We studied how the Lateral Occipital cortex represents object boundaries using fMRI.

Source code from Leonid Keselman's fork of Jon Barron's website