Mark Ibrahim
15 papers · 2021–2025 · 4 conferences · across top CS/AI conferences
Achievements
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🌍 Conference Polyglot (4) 🐝 Cross-Pollinator (11) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🌈 Renaissance Researcher (5)
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Keyword Pioneer
🌈
Renaissance Researcher
(5)
🤝
Dynamic Duo
(11)
👑
Triple Crown
💎
Century Club
(15)
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Prolific Year
(7)
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Keyword Collector
(60)
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The Questioner
Conferences
NIPS (8)
ICLR (4)
ICML (2)
CVPR (1)
Top co-authors
Keywords
computer vision
(3)
data augmentation
(3)
domain generalization
(3)
image classification
(2)
distribution shift
(2)
benchmark evaluation
(2)
vision transformer
(1)
object recognition
(1)
unified benchmark
(1)
visual reasoning
(1)
transfer learning
(1)
image generation
(1)
self-supervised learning
(1)
information retrieval
(1)
model evaluation
(1)
algorithmic fairness
(1)
multimodal learning
(1)
multi-modal learning
(1)
shortcut learning
(1)
representation learning
(1)
Papers
$\mathbb{X}$-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs
ICLR 2025
Discovering Environments with XRM
ICML 2024
UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling
NIPS 2024
The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More
NIPS 2024
Does Progress On Object Recognition Benchmarks Improve Generalization on Crowdsourced, Global Data?
ICLR 2024
Modeling Caption Diversity in Contrastive Vision-Language Pretraining
ICML 2024
PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning
NIPS 2023
Exploring Why Object Recognition Performance Degrades Across Income Levels and Geographies with Factor Annotations
NIPS 2023
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
NIPS 2023
A Whac-a-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others
CVPR 2023
Disentanglement of Correlated Factors via Hausdorff Factorized Support
ICLR 2023
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations
ICLR 2023
Understanding the detrimental class-level effects of data augmentation
NIPS 2023
Grounding inductive biases in natural images: invariance stems from variations in data
NIPS 2021
CrypTen: Secure Multi-Party Computation Meets Machine Learning
NIPS 2021