Ajay Subramanian
PhD student in Cognition & Perception
Department of Psychology
New York University
Work address: 6 Washington Place, New York, NY 10003
Email: ajay.subramanian@nyu.edu
Twitter: twitter.com/ajaysub110
Google Scholar: Ajay Subramanian
GitHub: github.com/ajaysub110
LinkedIn: linkedin.com/ajaysub110
I use psychophysics methods to probe the differences underlying human and machine vision. I am currently studying the computations that make human object recognition more robust than neural network recognition, and how these computations could help us develop more generalizable computer vision systems. I am a PhD student in Cognition & Perception at New York University, advised by Denis Pelli. Previously, I completed my undergraduate degree in Electronics and Communication Engineering at BITS Pilani, India. My broad interests are in deep learning inspired by and applied to human psychology and neuroscience.
Submitted papers and Preprints
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Subramanian, A., Sizikova, E., Majaj, N. J., Pelli, D. G. (submitted, 2023).
Spatial-frequency channels, shape bias, and adversarial robustness.
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Zhou, J. Y., Chun, C., Subramanian, A., Simoncelli, E. P. (submitted, 2023).
Comparing models of neural representation based on their metric tensors.
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Tsividis, P., Loula, J., Burga, J., Rodriguez, J. P., Arnaud, S., Foss, N., Campero, A., Subramanian, A., Pouncy, T., Gershman, S., Tenenbaum, J. B. (submitted to Nature, 2023).
Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning.
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Subramanian, A., Price, S., Sizikova, E., Kumbhar, O., Majaj, N. J., Pelli, D. G. (arXiv, 2022).
SATBench: Benchmarking the speed-accuracy tradeoff in object recognition by humans and dynamic neural networks.
arXiv.
Publications
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Subramanian, A., Chitlangia, S., Baths, V. (2022).
Reinforcement learning and its connections with neuroscience and psychology.
Neural Networks, 145, 271-287.
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Turner, J. P., Knight, J. C., Subramanian, A., Nowotny, T. (2022).
mlGeNN: accelerating SNN inference using GPU-enabled neural networks.
Neuromorphic Computing and Engineering, 2(2), 024002.
Conference presentations
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Subramanian, A., Sizikova, E., Majaj, N. J., Pelli, D. G. (2023).
Spatial-frequency channels for object recognition by neural networks are twice as wide as those of humans. An explanation for shape bias?
ECVP, Paphos, Cyprus.
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Subramanian, A., Sizikova, E., Majaj, N. J., Pelli, D. G. (2023).
Spatial-frequency channels for object recognition by neural networks are twice as wide as those of humans.
VSS Meeting, St. Pete Beach, USA.
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Zhou, J. Y., Chun, C., Subramanian, A., Simoncelli, E., P. (2023).
Computing and comparing metric tensors in neural response models.
VSS Meeting, St. Pete Beach, USA.
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Subramanian, A., Sizikova, E., Majaj, N. J., Pelli, D. G. (2023).
Spatial-frequency channels for object recognition by neural networks are twice as wide as those of humans.
COSYNE, Montreal, Canada.
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Subramanian, A., Price, S., Sizikova, E., Kumbhar, O., Majaj, N., Pelli, D. G. (2022).
Benchmarking dynamic neural-network models of the human speed-accuracy tradeoff.
VSS Meeting, St. Pete Beach, USA.
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Subramanian, A., Patil, R., Baths, V. (2022).
Word2Brain2Image: Visual Reconstruction from Spoken Word Representations.
ACCS, Goa, India.