Fartash Faghri
Fartash Faghri
Apple ML Research
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VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
F Faghri, DJ Fleet, JR Kiros, S Fidler
British Machine Vision Conference (BMVC), 2018
Technical report on the cleverhans v2.1.0 adversarial examples library
N Papernot, F Faghri, N Carlini, I Goodfellow, R Feinman, A Kurakin, ...
arXiv preprint arXiv:1610.00768, 2018
Adversarial Spheres
J Gilmer, L Metz, F Faghri, SS Schoenholz, M Raghu, M Wattenberg, ...
International Conference on Learning Representations (ICLR), Workshop Track, 2018
Adversarial Manipulation of Deep Representations
S Sabour, Y Cao, F Faghri, DJ Fleet
International Conference on Learning Representations (ICLR), 2016
Adaptive Gradient Quantization for Data-Parallel SGD
F Faghri, I Tabrizian, I Markov, D Alistarh, DM Roy, A Ramezani-Kebrya
Advances in neural information processing systems 33, 3174-3185, 2020
NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization
A Ramezani-Kebrya, F Faghri, I Markov, V Aksenov, D Alistarh, DM Roy
Journal of Machine Learning Research 22 (114), 1-43, 2021
SAM-CLIP: Merging Vision Foundation Models Towards Semantic and Spatial Understanding
H Wang, PKA Vasu, F Faghri, R Vemulapalli, M Farajtabar, S Mehta, ...
arXiv preprint arXiv:2310.15308, 2023
A Study of Gradient Variance in Deep Learning
F Faghri, D Duvenaud, DJ Fleet, J Ba
NeurIPS Workshop on Beyond First Order Methods, Conference on Neural …, 2020
Adversarial robustness through regularization: A second-order approach
A Ma, F Faghri, A Farahmand
arXiv preprint arXiv:2004.01832, 2020
MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training
PKA Vasu*, H Pouransari*, F Faghri*, R Vemulapalli, O Tuzel
Conference on Computer Vision and Pattern Recognition (CVPR), 2024
Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement
F Faghri, H Pouransari, S Mehta, M Farajtabar, A Farhadi, M Rastegari, ...
International Conference on Computer Vision (ICCV), 2023
Bridging the Gap Between Adversarial Robustness and Optimization Bias
F Faghri, S Gowal, C Vasconcelos, DJ Fleet, F Pedregosa, N Le Roux
ICLR Workshop on Security and Safety in Machine Learning Systems …, 2021
TiC-CLIP: Continual Training of CLIP Models
S Garg, M Farajtabar, H Pouransari, R Vemulapalli, S Mehta, O Tuzel, ...
International Conference on Learning Representations (ICLR), 2024
Weight Subcloning: Direct Initialization of Transformers Using Larger Pretrained Ones
M Samragh, M Farajtabar, S Mehta, R Vemulapalli, F Faghri, D Naik, ...
arXiv preprint arXiv:2312.09299, 2023
RangeAugment: Efficient online augmentation with range learning
S Mehta, S Naderiparizi, F Faghri, M Horton, L Chen, A Farhadi, O Tuzel, ...
arXiv preprint arXiv:2212.10553, 2022
MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks
A Ramezani-Kebrya, I Tabrizian, F Faghri, P Popovski
Transactions on Machine Learning Research (TMLR), 2022
Graph based semi-supervised human pose estimation: When the output space comes to help
N Pourdamghani, HR Rabiee, F Faghri, MH Rohban
Pattern Recognition Letters 33 (12), 1529-1535, 2012
FastFill: Efficient Compatible Model Update
F Jaeckle, F Faghri, A Farhadi, O Tuzel, H Pouransari
International Conference on Learning Representations (ICLR), 2023
DataComp-LM: In search of the next generation of training sets for language models
J Li, A Fang, G Smyrnis, M Ivgi, M Jordan, S Gadre, H Bansal, E Guha, ...
arXiv preprint arXiv:2406.11794, 2024
APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations
E Rosenfeld, P Nakkiran, H Pouransari, O Tuzel, F Faghri
NeurIPS Workshop Has it Trained Yet?, 2022
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