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2020 – today
- 2025
[i28]Jeffrey Li, Mohammadreza Armandpour, Iman Mirzadeh, Sachin Mehta, Vaishaal Shankar, Raviteja Vemulapalli, Samy Bengio, Oncel Tuzel, Mehrdad Farajtabar, Hadi Pouransari, Fartash Faghri:
TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining. CoRR abs/2504.02107 (2025)- [i27]Cheng-Yu Hsieh, Pavan Kumar Anasosalu Vasu, Fartash Faghri, Raviteja Vemulapalli, Chun-Liang Li, Ranjay Krishna, Oncel Tuzel, Hadi Pouransari:
FocalLens: Instruction Tuning Enables Zero-Shot Conditional Image Representations. CoRR abs/2504.08368 (2025) - 2024
- [j4]Mohammadreza Salehi, Mehrdad Farajtabar, Maxwell Horton, Fartash Faghri, Hadi Pouransari, Raviteja Vemulapalli, Oncel Tuzel, Ali Farhadi, Mohammad Rastegari, Sachin Mehta:
CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement. Trans. Mach. Learn. Res. 2024 (2024) - [c12]Haoxiang Wang, Pavan Kumar Anasosalu Vasu, Fartash Faghri, Raviteja Vemulapalli, Mehrdad Farajtabar, Sachin Mehta, Mohammad Rastegari, Oncel Tuzel, Hadi Pouransari:
SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding. CVPR Workshops 2024: 3635-3647 - [c11]Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel:
MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training. CVPR 2024: 15963-15974 - [c10]Jessica Maria Echterhoff, Fartash Faghri, Raviteja Vemulapalli, Ting-Yao Hu, Chun-Liang Li, Oncel Tuzel, Hadi Pouransari:
MUSCLE: A Model Update Strategy for Compatible LLM Evolution. EMNLP (Findings) 2024: 7320-7332 - [c9]Saurabh Garg, Mehrdad Farajtabar, Hadi Pouransari, Raviteja Vemulapalli, Sachin Mehta, Oncel Tuzel, Vaishaal Shankar, Fartash Faghri:
TiC-CLIP: Continual Training of CLIP Models. ICLR 2024 - [c8]Raviteja Vemulapalli, Hadi Pouransari, Fartash Faghri, Sachin Mehta, Mehrdad Farajtabar, Mohammad Rastegari, Oncel Tuzel:
Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific Models. ICML 2024 - [c7]Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash 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:
DataComp-LM: In search of the next generation of training sets for language models. NeurIPS 2024 - [i26]Sachin Mehta, Maxwell Horton, Fartash Faghri, Mohammad Hossein Sekhavat, Mahyar Najibi, Mehrdad Farajtabar, Oncel Tuzel, Mohammad Rastegari:
CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data. CoRR abs/2404.15653 (2024) - [i25]Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Oncel Tuzel:
CLIP with Quality Captions: A Strong Pretraining for Vision Tasks. CoRR abs/2405.08911 (2024) - [i24]Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Kumar Guha, Sedrick 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, Alexandros G. Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, Vaishaal Shankar:
DataComp-LM: In search of the next generation of training sets for language models. CoRR abs/2406.11794 (2024) - [i23]Jessica Maria Echterhoff, Fartash Faghri, Raviteja Vemulapalli, Ting-Yao Hu, Chun-Liang Li, Oncel Tuzel, Hadi Pouransari:
MUSCLE: A Model Update Strategy for Compatible LLM Evolution. CoRR abs/2407.09435 (2024) - [i22]Mohammad Samragh, Seyed-Iman Mirzadeh, Keivan Alizadeh-Vahid, Fartash Faghri, Minsik Cho, Moin Nabi, Devang Naik, Mehrdad Farajtabar:
Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization. CoRR abs/2409.12903 (2024) - [i21]Saleh Ashkboos, Iman Mirzadeh, Keivan Alizadeh, Mohammad Hossein Sekhavat, Moin Nabi, Mehrdad Farajtabar, Fartash Faghri:
Computational Bottlenecks of Training Small-scale Large Language Models. CoRR abs/2410.19456 (2024) - [i20]Pavan Kumar Anasosalu Vasu, Fartash Faghri, Chun-Liang Li, Cem Koc, Nate True, Albert Antony, Gokul Santhanam, James Gabriel, Peter Grasch, Oncel Tuzel, Hadi Pouransari:
FastVLM: Efficient Vision Encoding for Vision Language Models. CoRR abs/2412.13303 (2024) - 2023
- [c6]Fartash Faghri, Hadi Pouransari, Sachin Mehta, Mehrdad Farajtabar, Ali Farhadi, Mohammad Rastegari, Oncel Tuzel:
Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement. ICCV 2023: 16986-16997 - [c5]Florian Jaeckle, Fartash Faghri, Ali Farhadi, Oncel Tuzel, Hadi Pouransari:
FastFill: Efficient Compatible Model Update. ICLR 2023 - [i19]Florian Jaeckle, Fartash Faghri, Ali Farhadi, Oncel Tuzel, Hadi Pouransari:
FastFill: Efficient Compatible Model Update. CoRR abs/2303.04766 (2023) - [i18]Fartash Faghri, Hadi Pouransari, Sachin Mehta, Mehrdad Farajtabar, Ali Farhadi, Mohammad Rastegari, Oncel Tuzel:
Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement. CoRR abs/2303.08983 (2023) - [i17]Mohammadreza Salehi, Mehrdad Farajtabar, Maxwell Horton, Fartash Faghri, Hadi Pouransari, Raviteja Vemulapalli, Oncel Tuzel, Ali Farhadi, Mohammad Rastegari, Sachin Mehta:
CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement. CoRR abs/2310.14108 (2023) - [i16]Haoxiang Wang, Pavan Kumar Anasosalu Vasu, Fartash Faghri, Raviteja Vemulapalli, Mehrdad Farajtabar, Sachin Mehta, Mohammad Rastegari, Oncel Tuzel, Hadi Pouransari:
SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding. CoRR abs/2310.15308 (2023) - [i15]Saurabh Garg, Mehrdad Farajtabar, Hadi Pouransari, Raviteja Vemulapalli, Sachin Mehta, Oncel Tuzel, Vaishaal Shankar, Fartash Faghri:
TiC-CLIP: Continual Training of CLIP Models. CoRR abs/2310.16226 (2023) - [i14]Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel:
MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training. CoRR abs/2311.17049 (2023) - [i13]Raviteja Vemulapalli, Hadi Pouransari, Fartash Faghri, Sachin Mehta, Mehrdad Farajtabar, Mohammad Rastegari, Oncel Tuzel:
Label-efficient Training of Small Task-specific Models by Leveraging Vision Foundation Models. CoRR abs/2311.18237 (2023) - [i12]Mohammad Samragh, Mehrdad Farajtabar, Sachin Mehta, Raviteja Vemulapalli, Fartash Faghri, Devang Naik, Oncel Tuzel, Mohammad Rastegari:
Weight subcloning: direct initialization of transformers using larger pretrained ones. CoRR abs/2312.09299 (2023) - 2022
- [j3]Ali Ramezani-Kebrya, Iman Tabrizian, Fartash Faghri, Petar Popovski:
MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks. Trans. Mach. Learn. Res. 2022 (2022) - [i11]Ali Ramezani-Kebrya, Iman Tabrizian, Fartash Faghri, Petar Popovski:
MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks. CoRR abs/2207.07941 (2022) - [i10]Elan Rosenfeld, Preetum Nakkiran, Hadi Pouransari, Oncel Tuzel, Fartash Faghri:
APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations. CoRR abs/2210.03927 (2022) - [i9]Sachin Mehta, Saeid Naderiparizi, Fartash Faghri, Maxwell Horton, Lailin Chen, Ali Farhadi, Oncel Tuzel, Mohammad Rastegari:
RangeAugment: Efficient Online Augmentation with Range Learning. CoRR abs/2212.10553 (2022) - 2021
- [j2]Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan Alistarh, Daniel M. Roy:
NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization. J. Mach. Learn. Res. 22: 114:1-114:43 (2021) - [i8]Fartash Faghri, Cristina Nader Vasconcelos, David J. Fleet, Fabian Pedregosa, Nicolas Le Roux:
Bridging the Gap Between Adversarial Robustness and Optimization Bias. CoRR abs/2102.08868 (2021) - [i7]Fartash Faghri:
Training Efficiency and Robustness in Deep Learning. CoRR abs/2112.01423 (2021) - 2020
- [c4]Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel M. Roy, Ali Ramezani-Kebrya:
Adaptive Gradient Quantization for Data-Parallel SGD. NeurIPS 2020 - [i6]Avery Ma, Fartash Faghri, Amir-massoud Farahmand:
Adversarial Robustness through Regularization: A Second-Order Approach. CoRR abs/2004.01832 (2020) - [i5]Fartash Faghri, David Duvenaud, David J. Fleet, Jimmy Ba:
A Study of Gradient Variance in Deep Learning. CoRR abs/2007.04532 (2020) - [i4]Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel M. Roy, Ali Ramezani-Kebrya
:
Adaptive Gradient Quantization for Data-Parallel SGD. CoRR abs/2010.12460 (2020)
2010 – 2019
- 2019
- [i3]Ali Ramezani-Kebrya, Fartash Faghri, Daniel M. Roy:
NUQSGD: Improved Communication Efficiency for Data-parallel SGD via Nonuniform Quantization. CoRR abs/1908.06077 (2019) - 2018
- [c3]Fartash Faghri, David J. Fleet, Jamie Ryan Kiros, Sanja Fidler:
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives. BMVC 2018: 12 - [c2]Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow:
Adversarial Spheres. ICLR (Workshop) 2018 - [i2]Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow:
Adversarial Spheres. CoRR abs/1801.02774 (2018) - 2017
- [i1]Fartash Faghri, David J. Fleet, Jamie Ryan Kiros, Sanja Fidler:
VSE++: Improved Visual-Semantic Embeddings. CoRR abs/1707.05612 (2017) - 2016
- [c1]Sara Sabour, Yanshuai Cao, Fartash Faghri, David J. Fleet:
Adversarial Manipulation of Deep Representations. ICLR (Poster) 2016 - 2012
- [j1]Nima Pourdamghani, Hamid R. Rabiee, Fartash Faghri, Mohammad Hossein Rohban:
Graph based semi-supervised human pose estimation: When the output space comes to help. Pattern Recognit. Lett. 33(12): 1529-1535 (2012)
Coauthor Index
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