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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 when scaling to even larger models or deploying numerous per-user or per-task adapted models. In this work, we present Vector-based Random Matrix Adaptation (VeRA)1, which significantly reduces …
Publications ELoRA: Low-Rank Adaptation for Equivariant GNNs Chen Wang, Siyu Hu, Guangming Tan, Weile Jia Published: 01 May 2025, Last Modified: 23 Jul 2025 ICML 2025 poster
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 confabulations.
Publications ELoRA: Low-Rank Adaptation for Equivariant GNNs Chen Wang, Siyu Hu, Guangming Tan, Weile Jia Published: 01 May 2025, Last Modified: 23 Jul 2025 ICML 2025 poster
Promoting openness in scientific communication and the peer-review process
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 Published: 18 Jun 2024, Last Modified: 26 Jul 2024 ICML 2024 Workshop on LLMs and Cognition Poster Readers: Everyone
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 larger models, full...
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 model performance. This paper introduces LoRA-Pro to enhance LoRA’s performance by strategically adjusting the gradients of the two low-rank matrices, allowing the low-rank gradient to more …
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, ensuring physically consistent predictions, and (2) it leverages low-rank adaptations to significantly improve data efficiency.
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