Mark Ibrahim
SOFTWARE ENGINEER | NEW YORK CITY, UNITED STATES
How do we encode our intuitive ability to recognize the same dog while it's jumping during the day or hiding behind a tree at night? Mark is interested in building representations of the factors of variation in the world around us. Mark is exploring how tools from areas such as topology, group theory, and equivariant architectures can shed light on how representations can improve interpretability, robustness, and data-efficiency (semi- or self-supervised settings).
Research Areas
Mark's Work
Mark's Publications
December 18, 2024
CORE MACHINE LEARNING
UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling
Randall Balestriero, Diane Bouchacourt, Mark Ibrahim, Caner Hazirbas, Haider Al-Tahan, Quentin Garrido
December 18, 2024
December 12, 2024
COMPUTER VISION
EvalGIM: A Library for Evaluating Generative Image Models
Melissa Hall, Abhishek Charnalia, Adriana Romero Soriano, Candace Ross, Carolina Braga, Jakob Verbeek, Karen Ullrich, Maeve Ryan, Mark Ibrahim, Marton Havasi, Michal Drozdzal, Mike Rabbat, Oscar Mañas, Pietro Astolfi, Reyhane Askari, Tariq Berrada Ifriqi, Yohann Benchetrit
December 12, 2024
July 24, 2024
COMPUTER VISION
X-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs
Randall Balestriero, Diane Bouchacourt, Kyunghyun Cho, Mark Ibrahim, Pietro Astolfi, Vivien Cabannes, Vlad Sobal, Yann LeCun
July 24, 2024
June 05, 2024
CORE MACHINE LEARNING
An Introduction to Vision-Language Modeling
Anurag Ajay, Alexander C. Li, Suzanne Petryk, Zhiqiu Lin, Anas Mahmoud, Jun Chen, Mazda Moayeri, Aishwarya Agrawal, Florian Bordes, Adrien Bardes, Arjang Talattof, Asli Celikyilmaz, Bargav Jayaraman, Candace Ross, Chuan Guo, Diane Bouchacourt, Ellen Tan, Haider Al-Tahan, Hu Xu, Jonathan Lebensold, Kamalika Chaudhuri, Karen Ullrich, Karthik Padthe, Kate Saenko, Kushal Tirumala, Mark Ibrahim, Megan Richards, Melissa Hall, Oscar Mañas, Pietro Astolfi, Quentin Garrido, Reyhane Askari, Richard Pang, Rim Assouel, Samuel Lavoie, Srihari Jayakumar, Vasu Sharma, Vikas Chandra, Xilun Chen, Yunyang Xiong, Zechun Liu
June 05, 2024
November 09, 2021
COMPUTER VISION
CORE MACHINE LEARNING
Grounding inductive biases in natural images: invariance stems from variations in data
Diane Bouchacourt, Ari Morcos, Mark Ibrahim
November 09, 2021
October 18, 2021
CORE MACHINE LEARNING
CrypTen: Secure Multi-Party Computation Meets Machine Learning
Awni Hannun, Laurens van der Maaten, Brian Knott, Mark Ibrahim, Shobha Venkataraman, Shubho Sengupta
October 18, 2021
September 23, 2020
ML APPLICATIONS
Neural Relational Autoregression for High-Resolution COVID-19 Forecasting
Maximilian Nickel, Levent Sagun, Mark Ibrahim, Matt Le, Timothee Lacroix
September 23, 2020
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