2020

Machine learning

Bashivan P, Richards B, Rish I, Adversarial Feature Desensitization (submitted).

Machine learning

N Blanchard, J Kinnison, B RichardWebster, P Bashivan, WJ Scheirer, A Neurobiological Evaluation Metric for Neural Network Model Search, Computer Vision and Pattern Recognition (CVPR).

Neuroscience

Bashivan P, Kar K, DiCarlo J J, Neural Population Control via Deep Image Synthesis, Science.

2019

Neuroscience

Schrimpf M, Kubilius J, Hong H, Majaj N J, Rajalingham R, Issa E B, Kar K, Bashivan P, Prescott-Roy J,Schmidt K, Yamins DLK, DiCarlo J J (2019), Brain-like object recognition with high-performing shallow recurrent ANNs, NeurIPS.

Machine learning

Bashivan P, Schrimpf M, Ajemian R, Rish I, Riemer M, Tu Y, Continual Learning with Self-Organizing Maps, NIPS Workshop on Continual Learning.

Neuroscience

Arend L, Han Y, Schrimpf M, Bashivan P, Kar K, Poggio T, DiCarlo JJ, Boix X. Single units in adeep neural network functionally correspond with neurons in the brain: preliminary results. Center for Brains, Minds and Machines (CBMM).

Neuroscience

Bashivan P, Kar K, DiCarlo J J, Neural Population Control via Deep ANN ImageSynthesis, Computational Cognitive Neuroscience Conference (CCN).

2018

Neuroscience

Rajalingham R, Issa EB, Bashivan P, Kar K, Schmidt K, DiCarlo JJ, Large-scale, High-resolutionComparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks, Journal of Neuroscience.

Neuroscience

Dakka J, Bashivan P, Gheiratmand M, Rish I, Jha S, Greiner R, Learning Neural Markers ofSchizophrenia Disorder Using Recurrent Neural Networks, NIPS workshop on Machine Learning for Health.

Neuroscience

Rish I, Gheiratmand M, Cecchi G, Brown M, Greiner R, Polosecki P, Bashivan P, Greenshaw A,Ramasubbu R, and Dursun R, Learning Stable and Predictive Network-based Patterns of Schizophrenia and its Clinical Symptoms, Nature Schizophrenia.

2017

Neuroscience

Bashivan P, Yeasin M, Bidelman GM, Temporal Progression in Functional Connectivity DeterminesIndividual Differences in Working Memory Capacity, International Joint Conference on Neural Networks (IJCNN).

2016

Machine learning

Bashivan P, Rish I, Yeasin M, Codella NC, Learning Representations from EEG with DeepRecurrent-Convolutional Neural Networks, International Conference on Learning Representations (ICLR).

Machine learning

Bashivan P, Rish I, Heisig S, Mental State Recognition via Wearable EEG, Proceedings of NIPSworkshop on Machine Learning and Interpretation in Neuroimaging (MLINI15).

2015

Machine learning

Bashivan P, Yeasin M, Bidelman GM, Single trial prediction of normal and excessive cognitive loadthrough EEG feature fusion, Proceedings of IEEE Signal Processing in Medicine and Biology (SPMB).

Neuroscience

Bashivan P, Bidelman GM, Yeasin M, Modulation of Brain Connectivity by Memory Load in a Working Memory Network, Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI).

2014

Neuroscience

Bashivan P, Bidelman GM, Yeasin M, Spectrotemporal dynamics of the EEG during workingmemory encoding and maintenance predicts individual behavioral capacity, Eur. Journal of Neuroscience.

2013

Machine learning

Bashivan P, Bidelman GM, Yeasin M, Neural correlates of visual working memory load throughunsupervised spatial filtering of EEG, Proceedings of NIPS workshop on Machine Learning and Interpretation in Neuroimaging (MLINI13).

2012

Control engineering

Bashivan P, Fatehi A, Improved Switching for Multiple Model Adaptive Controller in NoisyEnvironment, Journal of Process Control.

Control engineering

Bashivan P, Fatehi A, Peymani E, Multiple-model control of pH neutralization plant using the SOM neural networks, Proceedings of IEEE Conference on Control, Communication and Automation (INDICON).

2008

Control engineering

Peymani E, Fatehi A, Bashivan P, and Khaki–Sedigh A, An Experimental Comparison of Adaptive Controllers on a pH Neutralization Pilot Plant, Proceedings of IEEE Conference on Control, Communication and Automation (INDICON).