Fartash Faghri | DeepAI

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∙ 03/15/2023

Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement

We propose Dataset Reinforcement, a strategy to improve a dataset once s...

0 Fartash Faghri, et al. ∙

∙ 03/08/2023

FastFill: Efficient Compatible Model Update

In many retrieval systems the original high dimensional data (e.g., imag...

0 Florian Jaeckle, et al. ∙

∙ 12/20/2022

RangeAugment: Efficient Online Augmentation with Range Learning

State-of-the-art automatic augmentation methods (e.g., AutoAugment and R...

0 Sachin Mehta, et al. ∙

∙ 10/08/2022

APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations

Recent advances in learning aligned multimodal representations have been...

13 Elan Rosenfeld, et al. ∙

∙ 07/16/2022

MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks

Implementations of SGD on distributed and multi-GPU systems creates new ...

0 Ali Ramezani-Kebrya, et al. ∙

∙ 12/02/2021

Training Efficiency and Robustness in Deep Learning

Deep Learning has revolutionized machine learning and artificial intelli...

0 Fartash Faghri, et al. ∙

∙ 04/28/2021

NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization

As the size and complexity of models and datasets grow, so does the need...

0 Ali Ramezani-Kebrya, et al. ∙

∙ 10/23/2020

Adaptive Gradient Quantization for Data-Parallel SGD

Many communication-efficient variants of SGD use gradient quantization s...

0 Fartash Faghri, et al. ∙

∙ 07/09/2020

A Study of Gradient Variance in Deep Learning

The impact of gradient noise on training deep models is widely acknowled...

20 Fartash Faghri, et al. ∙

∙ 04/04/2020

Adversarial Robustness through Regularization: A Second-Order Approach

Adversarial training is a common approach to improving the robustness of...

0 Avery Ma, et al. ∙

∙ 08/16/2019

NUQSGD: Improved Communication Efficiency for Data-parallel SGD via Nonuniform Quantization

As the size and complexity of models and datasets grow, so does the need...

4 Ali Ramezani-Kebrya, et al. ∙

∙ 01/09/2018

Adversarial Spheres

State of the art computer vision models have been shown to be vulnerable...

0 Justin Gilmer, et al. ∙

∙ 07/18/2017

VSE++: Improving Visual-Semantic Embeddings with Hard Negatives

We present a new technique for learning visual-semantic embeddings for c...

0 Fartash Faghri, et al. ∙

∙ 10/03/2016

cleverhans v2.0.0: an adversarial machine learning library

cleverhans is a software library that provides standardized reference im...

0 Nicolas Papernot, et al. ∙

∙ 11/16/2015

Adversarial Manipulation of Deep Representations

We show that the representation of an image in a deep neural network (DN...

0 Sara Sabour, et al. ∙

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