Fartash Faghri

According to our database, Fartash Faghri authored at least 38 papers between 2012 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting.

Chen Huang, Skyler Seto, Hadi Pouransari, Mehrdad Farajtabar, Raviteja Vemulapalli, Fartash Faghri, Oncel Tuzel, Barry-John Theobald, Joshua M. Susskind

CoRR, May, 2025

FocalLens: Instruction Tuning Enables Zero-Shot Conditional Image Representations.

Cheng-Yu Hsieh, Pavan Kumar Anasosalu Vasu, Fartash Faghri, Raviteja Vemulapalli, Chun-Liang Li, Ranjay Krishna, Oncel Tuzel, Hadi Pouransari

CoRR, April, 2025

MobileCLIP2: Improving Multi-Modal Reinforced Training.

Fartash Faghri, Pavan Kumar Anasosalu Vasu, Cem Koc, Vaishaal Shankar, Alexander T. Toshev, Oncel Tuzel, Hadi Pouransari

Trans. Mach. Learn. Res., 2025

FastVLM: Efficient Vision Encoding for Vision Language Models.

Pavan Kumar Anasosalu Vasu, Fartash Faghri, Chun-Liang Li, Cem Koc, Nate True, Albert Antony, Gokula Santhanam, James Gabriel, Peter Grasch, Oncel Tuzel, Hadi Pouransari

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining.

Jeffrey Li, Mohammadreza Armandpour, Iman Mirzadeh, Sachin Mehta, Vaishaal Shankar, Raviteja Vemulapalli, Samy Bengio, Oncel Tuzel, Mehrdad Farajtabar, Hadi Pouransari, Fartash Faghri

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024

CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement.

Mohammadreza Salehi, Mehrdad Farajtabar, Maxwell Horton, Fartash Faghri, Hadi Pouransari, Raviteja Vemulapalli, Oncel Tuzel, Ali Farhadi, Mohammad Rastegari, Sachin Mehta

Trans. Mach. Learn. Res., 2024

Computational Bottlenecks of Training Small-scale Large Language Models.

Saleh Ashkboos, Iman Mirzadeh, Keivan Alizadeh, Mohammad Hossein Sekhavat, Moin Nabi, Mehrdad Farajtabar, Fartash Faghri

CoRR, 2024

Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization.

Mohammad Samragh, Iman Mirzadeh, Keivan Alizadeh-Vahid, Fartash Faghri, Minsik Cho, Moin Nabi, Devang Naik, Mehrdad Farajtabar

CoRR, 2024

CLIP with Quality Captions: A Strong Pretraining for Vision Tasks.

Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Oncel Tuzel

CoRR, 2024

CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data.

Sachin Mehta, Maxwell Horton, Fartash Faghri, Mohammad Hossein Sekhavat, Mahyar Najibi, Mehrdad Farajtabar, Oncel Tuzel, Mohammad Rastegari

CoRR, 2024

DataComp-LM: In search of the next generation of training sets for language models.

Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Kumar Guha, Sedrick Scott Keh, Kushal Arora, Saurabh Garg, Rui Xin, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee F. Chen, Suchin Gururangan, Mitchell Wortsman, Alon Albalak, Yonatan Bitton, Marianna Nezhurina, Amro Abbas, Cheng-Yu Hsieh, Dhruba Ghosh, Josh Gardner, Maciej Kilian, Hanlin Zhang, Rulin Shao, Sarah M. Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Raghavi Chandu, Thao Nguyen, Igor Vasiljevic, Sham M. Kakade, Shuran Song, Sujay Sanghavi, Fartash Faghri, Sewoong Oh, Luke Zettlemoyer, Kyle Lo, Alaaeldin El-Nouby, Hadi Pouransari, Alexander Toshev, Stephanie Wang, Dirk Groeneveld, Luca Soldaini, Pang Wei Koh, Jenia Jitsev, Thomas Kollar, Alex Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, Vaishaal Shankar

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific Models.

Raviteja Vemulapalli, Hadi Pouransari, Fartash Faghri, Sachin Mehta, Mehrdad Farajtabar, Mohammad Rastegari, Oncel Tuzel

Proceedings of the Forty-first International Conference on Machine Learning, 2024

TiC-CLIP: Continual Training of CLIP Models.

Saurabh Garg, Mehrdad Farajtabar, Hadi Pouransari, Raviteja Vemulapalli, Sachin Mehta, Oncel Tuzel, Vaishaal Shankar, Fartash Faghri

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Scaling Smart: Accelerating Large Language Model Pre-Training with Small Model Initialization.

Mohammad Samragh, Seyed-Iman Mirzadeh, Keivan Alizadeh-Vahid, Fartash Faghri, Minsik Cho, Moin Nabi, Devang Naik, Mehrdad Farajtabar

Proceedings of the NeurIPS Efficient Natural Language and Speech Processing Workshop, 2024

Computational Bottlenecks of Training Small-scale Large Language Models.

Saleh Ashkboos, Seyed-Iman Mirzadeh, Keivan Alizadeh-Vahid, Mohammad Hossein Sekhavat, Moin Nabi, Mehrdad Farajtabar, Fartash Faghri

Proceedings of the NeurIPS Efficient Natural Language and Speech Processing Workshop, 2024

MUSCLE: A Model Update Strategy for Compatible LLM Evolution.

Jessica Maria Echterhoff, Fartash Faghri, Raviteja Vemulapalli, Ting-Yao Hu, Chun-Liang Li, Oncel Tuzel, Hadi Pouransari

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding.

Haoxiang Wang, Pavan Kumar Anasosalu Vasu, Fartash Faghri, Raviteja Vemulapalli, Mehrdad Farajtabar, Sachin Mehta, Mohammad Rastegari, Oncel Tuzel, Hadi Pouransari

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training.

Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

Weight subcloning: direct initialization of transformers using larger pretrained ones.

Mohammad Samragh, Mehrdad Farajtabar, Sachin Mehta, Raviteja Vemulapalli, Fartash Faghri, Devang Naik, Oncel Tuzel, Mohammad Rastegari

CoRR, 2023

Label-efficient Training of Small Task-specific Models by Leveraging Vision Foundation Models.

Raviteja Vemulapalli, Hadi Pouransari, Fartash Faghri, Sachin Mehta, Mehrdad Farajtabar, Mohammad Rastegari, Oncel Tuzel

CoRR, 2023

FastFill: Efficient Compatible Model Update.

Florian Jaeckle, Fartash Faghri, Ali Farhadi, Oncel Tuzel, Hadi Pouransari

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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

Fartash Faghri, Hadi Pouransari, Sachin Mehta, Mehrdad Farajtabar, Ali Farhadi, Mohammad Rastegari, Oncel Tuzel

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

Training Efficiency and Robustness in Deep Learning

Fartash Faghri

PhD thesis, 2022

MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks.

Ali Ramezani-Kebrya, Iman Tabrizian, Fartash Faghri, Petar Popovski

Trans. Mach. Learn. Res., 2022

RangeAugment: Efficient Online Augmentation with Range Learning.

Sachin Mehta, Saeid Naderiparizi, Fartash Faghri, Maxwell Horton, Lailin Chen, Ali Farhadi, Oncel Tuzel, Mohammad Rastegari

CoRR, 2022

APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations.

Elan Rosenfeld, Preetum Nakkiran, Hadi Pouransari, Oncel Tuzel, Fartash Faghri

CoRR, 2022

2021

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

Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan Alistarh, Daniel M. Roy

J. Mach. Learn. Res., 2021

Training Efficiency and Robustness in Deep Learning.

Fartash Faghri

CoRR, 2021

Bridging the Gap Between Adversarial Robustness and Optimization Bias.

Fartash Faghri, Cristina Nader Vasconcelos, David J. Fleet, Fabian Pedregosa, Nicolas Le Roux

CoRR, 2021

2020

A Study of Gradient Variance in Deep Learning.

Fartash Faghri, David Duvenaud, David J. Fleet, Jimmy Ba

CoRR, 2020

Adversarial Robustness through Regularization: A Second-Order Approach.

Avery Ma, Fartash Faghri, Amir-massoud Farahmand

CoRR, 2020

Adaptive Gradient Quantization for Data-Parallel SGD.

Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel M. Roy, Ali Ramezani-Kebrya

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

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

Ali Ramezani-Kebrya, Fartash Faghri, Daniel M. Roy

CoRR, 2019

2018

Adversarial Spheres.

Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow

Proceedings of the 6th International Conference on Learning Representations, 2018

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

Fartash Faghri, David J. Fleet, Jamie Ryan Kiros, Sanja Fidler

Proceedings of the British Machine Vision Conference 2018, 2018

2017

VSE++: Improved Visual-Semantic Embeddings.

Fartash Faghri, David J. Fleet, Jamie Ryan Kiros, Sanja Fidler

CoRR, 2017

2016

Adversarial Manipulation of Deep Representations.

Sara Sabour, Yanshuai Cao, Fartash Faghri, David J. Fleet

Proceedings of the 4th International Conference on Learning Representations, 2016

2012

Graph based semi-supervised human pose estimation: When the output space comes to help.

Nima Pourdamghani, Hamid R. Rabiee, Fartash Faghri, Mohammad Hossein Rohban

Pattern Recognit. Lett., 2012