no code implementations • 26 Jul 2021 • Alberto Santamaria-Pang, Jianwei Qiu, Aritra Chowdhury, James Kubricht, Peter Tu, Iyer Naresh, Nurali Virani
Third, we generate new adversarial images by projecting back the original coefficients from the low scale and the perturbed coefficients from the high scale sub-space.
no code implementations • 22 Aug 2020 • Aritra Chowdhury, Alberto Santamaria-Pang, James R. Kubricht, Jianwei Qiu, Peter Tu
We show state of the art results for segmentation of COVID-19 lung infections in CT.
1 code implementation • 22 Aug 2020 • Aritra Chowdhury, Alberto Santamaria-Pang, James R. Kubricht, Peter Tu
In this work, we demonstrate for the first time, the emer-gence of deep symbolic representations of emergent language in the frame-work of image classification.
no code implementations • 27 Jul 2020 • Wufei Ma, Elizabeth Kautz, Arun Baskaran, Aritra Chowdhury, Vineet Joshi, Bülent Yener, Daniel Lewis
A binary alloy (uranium-molybdenum) that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions.
no code implementations • 18 Jul 2020 • Alberto Santamaria-Pang, Anup Sood, Dan Meyer, Aritra Chowdhury, Fiona Ginty
We present a method for automatic cell classification in tissue samples using an automated training set from multiplexed immunofluorescence images.
no code implementations • 18 Jul 2020 • Alberto Santamaria-Pang, James Kubricht, Aritra Chowdhury, Chitresh Bhushan, Peter Tu
A UNet-like architecture is used to generate input to the Sender network which produces a symbolic sentence, and a Receiver network co-generates the segmentation mask based on the sentence.
no code implementations • 18 Jul 2020 • Aritra Chowdhury, James R. Kubricht, Anup Sood, Peter Tu, Alberto Santamaria-Pang
In one form of the game, a sender and a receiver observe a set of cells from 5 different cell phenotypes.
no code implementations • 7 Oct 2019 • Xiaomeng Dong, Jun-Pyo Hong, Hsi-Ming Chang, Michael Potter, Aritra Chowdhury, Purujit Bahl, Vivek Soni, Yun-chan Tsai, Rajesh Tamada, Gaurav Kumar, Caroline Favart, V. Ratna Saripalli, Gopal Avinash
As the complexity of state-of-the-art deep learning models increases by the month, implementation, interpretation, and traceability become ever-more-burdensome challenges for AI practitioners around the world.
no code implementations • 25 Feb 2019 • Aritra Chowdhury, Malik Magdin-Ismail, Bulent Yener
We show that algorithm selection and hyper-parameter optimization methods can be used to quantify the error contribution and that random search is able to quantify the contribution more accurately than Bayesian optimization.
1 code implementation • 21 Feb 2019 • Aritra Chowdhury, Malik Magdon-Ismail, Bulent Yener
The agnostic and naive methodologies quantify the error contribution and propagation respectively from the computational steps, algorithms and hyperparameters in the image classification pipeline.