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)

Adversarial Manipulation of Deep Representations. - Sara Sabour; Yanshuai Cao; Fartash Faghri; David J. Fleet (ICLR (Poster), 2016)

VSE++: Improved Visual-Semantic Embeddings. - Fartash Faghri; David J. Fleet; Jamie Ryan Kiros; Sanja Fidler (CoRR, 2017)

VSE++: Improving Visual-Semantic Embeddings with Hard Negatives. - Fartash Faghri; David J. Fleet; Jamie Ryan Kiros; Sanja Fidler (BMVC, 2018)

Adversarial Spheres. - Justin Gilmer; Luke Metz; Fartash Faghri; Samuel S. Schoenholz; Maithra Raghu; Martin Wattenberg; Ian J. Goodfellow (ICLR (Workshop), 2018)

NUQSGD: Improved Communication Efficiency for Data-parallel SGD via Nonuniform Quantization. - Ali Ramezani-Kebrya; Fartash Faghri; Daniel M. Roy 001 (CoRR, 2019)

Adaptive Gradient Quantization for Data-Parallel SGD. - Fartash Faghri; Iman Tabrizian; Ilia Markov; Dan Alistarh; Daniel M. Roy 001; Ali Ramezani-Kebrya (NeurIPS, 2020)

Adversarial Robustness through Regularization: A Second-Order Approach. - Avery Ma; Fartash Faghri; Amir-massoud Farahmand (CoRR, 2020)

A Study of Gradient Variance in Deep Learning. - Fartash Faghri; David Duvenaud; David J. Fleet; Jimmy Ba (CoRR, 2020)

Bridging the Gap Between Adversarial Robustness and Optimization Bias. - Fartash Faghri; Cristina Nader Vasconcelos; David J. Fleet; Fabian Pedregosa; Nicolas Le Roux (CoRR, 2021)

Training Efficiency and Robustness in Deep Learning. - Fartash Faghri (CoRR, 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 001 (J. Mach. Learn. Res., 2021)

APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations. - Elan Rosenfeld; Preetum Nakkiran; Hadi Pouransari; Oncel Tuzel; Fartash Faghri (CoRR, 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)

MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks. - Ali Ramezani-Kebrya; Iman Tabrizian; Fartash Faghri; Petar Popovski (Trans. Mach. Learn. Res., 2022)

Training Efficiency and Robustness in Deep Learning - Fartash Faghri (2022)

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 (ICCV, 2023)

FastFill: Efficient Compatible Model Update. - Florian Jaeckle; Fartash Faghri; Ali Farhadi; Oncel Tuzel; Hadi Pouransari (ICLR, 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)

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)

MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training. - Pavan Kumar Anasosalu Vasu; Hadi Pouransari; Fartash Faghri; Raviteja Vemulapalli; Oncel Tuzel (CVPR, 2024)

SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding. - Haoxiang Wang 003; Pavan Kumar Anasosalu Vasu; Fartash Faghri; Raviteja Vemulapalli; Mehrdad Farajtabar; Sachin Mehta; Mohammad Rastegari; Oncel Tuzel; Hadi Pouransari (CVPR Workshops, 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 (EMNLP (Findings), 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 (ENLSP, 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 (ENLSP, 2024)

TiC-CLIP: Continual Training of CLIP Models. - Saurabh Garg; Mehrdad Farajtabar; Hadi Pouransari; Raviteja Vemulapalli; Sachin Mehta; Oncel Tuzel; Vaishaal Shankar; Fartash Faghri (ICLR, 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 (ICML, 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; et al. (NeurIPS, 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)

CLIP with Quality Captions: A Strong Pretraining for Vision Tasks. - Pavan Kumar Anasosalu Vasu; Hadi Pouransari; Fartash Faghri; Oncel Tuzel (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)

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)

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)

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 (ACL (1), 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 (CVPR, 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)

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, 2025)

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting. - Chen Huang 001; Skyler Seto; Hadi Pouransari; Mehrdad Farajtabar; Raviteja Vemulapalli; Fartash Faghri; Oncel Tuzel; Barry-John Theobald; Joshua M. Susskind (CoRR, 2025)