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      22 thg 1, 2025 · LoRA reduces the parameters required during training by introducing a low-rank matrix, thereby reducing computational requirements and memory footprint while maintaining …

      28 thg 1, 2022 · An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train …

      27 thg 10, 2023 · Foundation models (FMs) in massive parameter space pretrained on a large amount of (public) data perform remarkably well on various downstream tasks with just a few …

      Rohini Elora Das Undergrad student, New York University Joined May 2024

      ABSTRACT Low-rank adapation (LoRA) is a popular method that reduces the number of train-able parameters when finetuning large language models, but still faces acute stor-age challenges …

      We develop a PEFT strategy tailored for SO(3) equiv-ariant GNN models, called ELoRA (Equivariant Low-Rank Adaptation). We prove that ELoRA can preserve the equivariance during fine-tuning. …

      17 thg 6, 2024 · TL;DR: We use iterative Theory of Mind tests to reveal limitations in current multimodal AI’s ability to create a consistent world model and we identify new multimodal …

      18 thg 6, 2024 · ICML 2024 Workshop LLMs and Cognition Submissions Iterative Theory of Mind Assay of Multimodal AI Models Rohini Elora Das, Rajarshi Das, Niharika Maity, Sreerupa Das …

      Promoting openness in scientific communication and the peer-review process

      1 thg 5, 2025 · ELoRA adopts a path-dependent decomposition for weights updating which offers two key advantages: (1) it preserves SO (3) equivariance throughout the fine-tuning process, …

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