Search Results for author: Pouya Bashivan

Found 12 papers, 6 papers with code

Towards Out-of-Distribution Adversarial Robustness

1 code implementation6 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.

Adversarial Robustness

Learning Robust Kernel Ensembles with Kernel Average Pooling

no code implementations30 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.

Adversarial Feature Desensitization

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.

Adversarial Robustness Domain Adaptation +1

Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?

1 code implementation2 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.

Object Recognition

Aligning Artificial Neural Networks to the Brain yields Shallow Recurrent Architectures

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.

Anatomy Object Categorization

Continual Learning with Self-Organizing Maps

no code implementations19 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.

Continual Learning

Teacher Guided Architecture Search

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.

Computational Efficiency Neural Architecture Search

A Neurobiological Evaluation Metric for Neural Network Model Search

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.

Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks

no code implementations1 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.

Mental State Recognition via Wearable EEG

no code implementations2 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.


Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

11 code implementations19 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.

EEG General Classification +3

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