Search Results for author: Shekoofeh Azizi

Found 22 papers, 8 papers with code

Radial Basis Feature Transformation to Arm CNNs Against Adversarial Attacks

no code implementations ICLR 2019 Saeid Asgari Taghanaki, Shekoofeh Azizi, Ghassan Hamarneh

The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations.

Image Classification

Health AI Developer Foundations

no code implementations22 Nov 2024 Atilla P. Kiraly, Sebastien Baur, Kenneth Philbrick, Fereshteh Mahvar, Liron Yatziv, Tiffany Chen, Bram Sterling, Nick George, Fayaz Jamil, Jing Tang, Kai Bailey, Faruk Ahmed, Akshay Goel, Abbi Ward, Lin Yang, Andrew Sellergren, Yossi Matias, Avinatan Hassidim, Shravya Shetty, Daniel Golden, Shekoofeh Azizi, David F. Steiner, Yun Liu, Tim Thelin, Rory Pilgrim, Can Kirmizibayrak

Finally, while HAI-DEF and specifically the foundation models lower the barrier to entry for ML in healthcare, we emphasize the importance of validation with problem- and population-specific data for each desired usage setting.

Fairness

Decoding Visual Experience and Mapping Semantics through Whole-Brain Analysis Using fMRI Foundation Models

1 code implementation11 Nov 2024 Yanchen Wang, Adam Turnbull, Tiange Xiang, Yunlong Xu, Sa Zhou, Adnan Masoud, Shekoofeh Azizi, Feng Vankee Lin, Ehsan Adeli

Neural decoding, the process of understanding how brain activity corresponds to different stimuli, has been a primary objective in cognitive sciences.

Contrastive Learning

Tx-LLM: A Large Language Model for Therapeutics

no code implementations10 Jun 2024 Juan Manuel Zambrano Chaves, Eric Wang, Tao Tu, Eeshit Dhaval Vaishnav, Byron Lee, S. Sara Mahdavi, Christopher Semturs, David Fleet, Vivek Natarajan, Shekoofeh Azizi

Developing therapeutics is a lengthy and expensive process that requires the satisfaction of many different criteria, and AI models capable of expediting the process would be invaluable.

Drug Discovery Language Modeling +2

Towards Accurate Differential Diagnosis with Large Language Models

no code implementations30 Nov 2023 Daniel McDuff, Mike Schaekermann, Tao Tu, Anil Palepu, Amy Wang, Jake Garrison, Karan Singhal, Yash Sharma, Shekoofeh Azizi, Kavita Kulkarni, Le Hou, Yong Cheng, Yun Liu, S Sara Mahdavi, Sushant Prakash, Anupam Pathak, Christopher Semturs, Shwetak Patel, Dale R Webster, Ewa Dominowska, Juraj Gottweis, Joelle Barral, Katherine Chou, Greg S Corrado, Yossi Matias, Jake Sunshine, Alan Karthikesalingam, Vivek Natarajan

Comparing the two assisted study arms, the DDx quality score was higher for clinicians assisted by our LLM (top-10 accuracy 51. 7%) compared to clinicians without its assistance (36. 1%) (McNemar's Test: 45. 7, p < 0. 01) and clinicians with search (44. 4%) (4. 75, p = 0. 03).

Synthetic Data from Diffusion Models Improves ImageNet Classification

no code implementations17 Apr 2023 Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia, Mohammad Norouzi, David J. Fleet

Deep generative models are becoming increasingly powerful, now generating diverse high fidelity photo-realistic samples given text prompts.

Classification Data Augmentation

Joint Debiased Representation and Image Clustering Learning with Self-Supervision

no code implementations14 Sep 2022 Shunjie-Fabian Zheng, JaeEun Nam, Emilio Dorigatti, Bernd Bischl, Shekoofeh Azizi, Mina Rezaei

However, existing methods for joint clustering and contrastive learning do not perform well on long-tailed data distributions, as majority classes overwhelm and distort the loss of minority classes, thus preventing meaningful representations to be learned.

Clustering Contrastive Learning +2

Deep Bregman Divergence for Contrastive Learning of Visual Representations

no code implementations15 Sep 2021 Mina Rezaei, Farzin Soleymani, Bernd Bischl, Shekoofeh Azizi

In this paper, we propose deep Bregman divergences for contrastive learning of visual representation where we aim to enhance contrastive loss used in self-supervised learning by training additional networks based on functional Bregman divergence.

Contrastive Learning object-detection +2

Big Self-Supervised Models Advance Medical Image Classification

1 code implementation ICCV 2021 Shekoofeh Azizi, Basil Mustafa, Fiona Ryan, Zachary Beaver, Jan Freyberg, Jonathan Deaton, Aaron Loh, Alan Karthikesalingam, Simon Kornblith, Ting Chen, Vivek Natarajan, Mohammad Norouzi

Self-supervised pretraining followed by supervised fine-tuning has seen success in image recognition, especially when labeled examples are scarce, but has received limited attention in medical image analysis.

Contrastive Learning General Classification +4

Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting

6 code implementations CVPR 2020 Zili Yi, Qiang Tang, Shekoofeh Azizi, Daesik Jang, Zhan Xu

Since convolutional layers of the neural network only need to operate on low-resolution inputs and outputs, the cost of memory and computing power is thus well suppressed.

2k 8k +2

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