Fartash Faghri

Reseach Scientist at Apple
< firstname > [at] apple [dot] com

PhD
Department of Computer Science
University of Toronto
Supervisor: David Fleet
< lastname > [at] cs.toronto [dot] edu

Google Scholar / Semantic Scholar / Twitter / Github / LinkedIn

Research

I'm interested in machine learning, optimization, and computer vision. My recent research has been about efficient and robust training in deep learning.

	Bridging the Gap Between Adversarial Robustness and Optimization Bias Fartash Faghri, Cristina Vasconcelos, David J. Fleet, Fabian Pedregosa, Nicolas Le Roux arXiv, 2021 arXiv / code Some standard models are maximally robust with no effort. Fourier-Linf attack is the one linear convolutional networks are maximally robust against.
	Adaptive Gradient Quantization for Data-Parallel SGD Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel Roy, Ali Ramezani-Kebrya NeurIPS, 2020 arXiv / code / video Same accuracy as 32 bit gradients with only 3 quantization bits.
	A Study of Gradient Variance in Deep Learning Fartash Faghri, David Duvenaud, David J. Fleet, Jimmy Ba NeurIPS workshop, 2019 on Beyond First Order Methods in ML. (Title: Gluster: Variance Reduced Mini-Batch SGD with Gradient Clustering) arXiv / code An efficient method for clustering gradients of training data. Observations on the variance of gradients during training for standard deep learning models.
	SOAR: Second-Order Adversarial Regularization Avery Ma, Fartash Faghri, Nicolas Papernot, Amir-massoud Farahmand ArXiv, 2020 arXiv A second-order adversarial regularizer based on the Taylor approximation of the inner-max in the robust optimization objective.
	NUQSGD: Improved Communication Efficiency for Data-parallel SGD via Nonuniform Quantization Ali Ramezani-Kebrya, Fartash Faghri, Daniel M. Roy ArXiv, 2019 arXiv An efficient gradient quantization method with convergence guaruantees.
	Adversarial Spheres Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian Goodfellow ICLR workshop, 2018 arXiv A synthetic example for studying the relationship between high-dimensional geometry and adversarial examples.
	VSE++: Improving Visual-Semantic Embeddings with Hard Negatives Fartash Faghri, David J. Fleet, Jamie Ryan Kiros, Sanja Fidler BMVC, 2018 (Spotlight) arXiv / code / video A simple change to common loss functions used for multi-modal embeddings. That, combined with fine-tuning and use of augmented data, yields significant gains in retrieval performance.
	Adversarial Manipulation of Deep Representations Sara Sabour, Yanshuai Cao, Fartash Faghri, David J. Fleet ICLR, 2016 arXiv / code A feature adversary is a new type of adversarial image that its internal representation appears remarkably similar to a different image.

Service

Reviewer: ICLR (2021, 2020, 2019), ICML 2021, NeurIPS 2020, ECCV 2020, ICCV 2021, ICLR 2020 Workshop on Trustworthy ML, NeurIPS 2018 Workshop on Security in Machine Learning.

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