1 code implementation • 6 Oct 2022 • Adam Ibrahim, Charles Guille-Escuret, Ioannis Mitliagkas, Irina Rish, David Krueger, Pouya Bashivan
Compared to existing methods, we obtain similar or superior worst-case adversarial robustness on attacks seen during training.
no code implementations • 30 Sep 2022 • Pouya Bashivan, Adam Ibrahim, Amirozhan Dehghani, Yifei Ren
Model ensembles have long been used in machine learning to reduce the variance in individual model predictions, making them more robust to input perturbations.
1 code implementation • NeurIPS 2021 • Pouya Bashivan, Reza Bayat, Adam Ibrahim, Kartik Ahuja, Mojtaba Faramarzi, Touraj Laleh, Blake Aaron Richards, Irina Rish
Our method, called Adversarial Feature Desensitization (AFD), aims at learning features that are invariant towards adversarial perturbations of the inputs.
1 code implementation • 2 Jan 2020 • Martin Schrimpf, Jonas Kubilius, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Franziska Geiger, Kailyn Schmidt, Daniel L. K. Yamins, James J. DiCarlo
We therefore developed Brain-Score – a composite of multiple neural and behavioral benchmarks that score any ANN on how similar it is to the brain’s mechanisms for core object recognition – and we deployed it to evaluate a wide range of state-of-the-art deep ANNs.
2 code implementations • NeurIPS 2019 • Jonas Kubilius, Martin Schrimpf, Kohitij Kar, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Pouya Bashivan, Jonathan Prescott-Roy, Kailyn Schmidt, Aran Nayebi, Daniel Bear, Daniel L. K. Yamins, James J. DiCarlo
Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream.
no code implementations • ICLR 2019 • Jonas Kubilius, Martin Schrimpf, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Kailyn Schmidt, Aran Nayebi, Daniel Bear, Daniel L. K. Yamins, James J. DiCarlo
Deep artificial neural networks with spatially repeated processing (a. k. a., deep convolutional ANNs) have been established as the best class of candidate models of visual processing in the primate ventral visual processing stream.
no code implementations • 19 Apr 2019 • Pouya Bashivan, Martin Schrimpf, Robert Ajemian, Irina Rish, Matthew Riemer, Yuhai Tu
Most previous approaches to this problem rely on memory replay buffers which store samples from previously learned tasks, and use them to regularize the learning on new ones.
no code implementations • ICCV 2019 • Pouya Bashivan, Mark Tensen, James J. DiCarlo
We further show that measurements from only ~300 neurons from primate visual system provides enough signal to find a network with an Imagenet top-1 error that is significantly lower than that achieved by performance-guided architecture search alone.
1 code implementation • CVPR 2019 • Nathaniel Blanchard, Jeffery Kinnison, Brandon RichardWebster, Pouya Bashivan, Walter J. Scheirer
In this paper we introduce a human-model similarity (HMS) metric, which quantifies the similarity of human fMRI and network activation behavior.
no code implementations • 1 Dec 2017 • Jumana Dakka, Pouya Bashivan, Mina Gheiratmand, Irina Rish, Shantenu Jha, Russell Greiner
Smart systems that can accurately diagnose patients with mental disorders and identify effective treatments based on brain functional imaging data are of great applicability and are gaining much attention.
no code implementations • 2 Feb 2016 • Pouya Bashivan, Irina Rish, Steve Heisig
The increasing quality and affordability of consumer electroencephalogram (EEG) headsets make them attractive for situations where medical grade devices are impractical.
11 code implementations • 19 Nov 2015 • Pouya Bashivan, Irina Rish, Mohammed Yeasin, Noel Codella
One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with such data.