Παρακολούθηση
Fartash Faghri
Fartash Faghri
Apple ML Research
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα cs.toronto.edu
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Παρατίθεται από
Παρατίθεται από
Έτος
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
F Faghri, DJ Fleet, JR Kiros, S Fidler
British Machine Vision Conference (BMVC), 2018
15562018
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
995*2018
Adversarial Spheres
J Gilmer, L Metz, F Faghri, SS Schoenholz, M Raghu, M Wattenberg, ...
International Conference on Learning Representations (ICLR), Workshop Track, 2018
469*2018
Adversarial Manipulation of Deep Representations
S Sabour, Y Cao, F Faghri, DJ Fleet
International Conference on Learning Representations (ICLR), 2016
3562016
SAM-CLIP: Merging Vision Foundation Models Towards Semantic and Spatial Understanding
H Wang, PKA Vasu, F Faghri, R Vemulapalli, M Farajtabar, S Mehta, ...
CVPR 2024 eLVM Workshop, 2024
1292024
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 (NeurIPS) 33, 3174-3185, 2020
962020
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, ...
Advances in neural information processing systems (NeurIPS), 2024
67*2024
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
59*2021
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
302024
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
26*2020
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
222024
Soar: Second-order adversarial regularization
A Ma, F Faghri, N Papernot, A Farahmand
arXiv preprint arXiv:2004.01832, 2020
22*2020
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
152023
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
112023
Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific Models
R Vemulapalli, H Pouransari, F Faghri, S Mehta, M Farajtabar, ...
International Conference on Machine Learning (ICML), 2024
9*2024
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
92021
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
82022
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
72022
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
62022
CLIP with Quality Captions: A Strong Pretraining for Vision Tasks
PKA Vasu, H Pouransari, F Faghri, O Tuzel
arXiv preprint arXiv:2405.08911, 2024
52024
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