
Mark Ibrahim
@marksibrahim
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Researching the dark arts of deep learning at Meta's FAIR (Fundamental AI Research) Lab
everywhere
Joined December 2012
RT @karen_ullrich: How would you make an LLM "forget" the concept of dog — or any other arbitrary concept? 🐶❓. We introduce SAMD & SAMI — a….
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RT @polkirichenko: Join us at #CVPR2025 Demographic Diversity in Computer Vision workshop tomorrow!.📅 Wednesday, June 11, 9am-6pm.📍 room 21….
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Join us as a PhD research intern at FAIR w/.@polkirichenko .@kamalikac .to start this summer or fall with a focus on open science into multimodal models, agents and beyond! Email polkirichenko@meta.com with the title [Prospective Intern 2025] and attach your CV if interested!.
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RT @garridoq_: The last paper of my PhD is finally out ! Introducing."Intuitive physics understanding emerges from self-supervised pretrain….
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RT @vlad_is_ai: 𝕏-CLR got accepted to ICLR 2025 @iclr_conf! See you in Singapore!.It was also recently mentioned in The Batch by @DeepLearn….
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RT @haidertahan: 🚀 Excited to share our work at #NeurIPS2024! We show how billion parameter VLMs lose to a two-layer MLP on MNIST. Come by….
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RT @hall__melissa: Work done w/ amazing collaborators @oscmansan, @ReyhaneAskari, @marksibrahim, @candacerossio, @Piovrasca, Tariq Berrada,….
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We find MLM-U improves knowledge retrieval on Wikipedia-based questions and even outperforms a pretrained 7B Mistral model with a much smaller 100M parameter transformer trained from scratch! Come by our NeurIPS poster Exhibit Halls A-C #3204 11am PST on Thursday to learn more!.
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Can we boost transformers’ ability to retrieve knowledge and plan in maze navigation by only tweaking the learning objective? We emphatically say YES in our NeurIPS 2024 study! 🧵. w/ @WKitouni, Niklas Nolte, Mike Rabbat, @D_Bouchacourt , @adinamwilliams
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RT @AIatMeta: New research from Meta FAIR: UniBench is a unified implementation of 50+ VLM benchmarks spanning a comprehensive range of car….
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RT @_akhaliq: Meta announces UniBench. Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling. discuss: .
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RT @ylecun: A soft similarity graph improves contrastive learning for image recognition. By @vlad_is_ai and a cast of characters from Meta….
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RT @vlad_is_ai: Representation learning is often done by considering samples to be either identical (same class, positive pairs) or not–wit….
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