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Monarch: Expressive Structured Matrices for Efficient and Accurate Training.

T. Dao, B. Chen, N. Sohoni, A. Desai, M. Poli, J. Grogan, A. Liu, A. Rao, A. Rudra, и C. Ré. ICML, том 162 из Proceedings of Machine Learning Research, стр. 4690-4721. PMLR, (2022)

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Dao Nguyen

Dao Luong

Lu Dao

Abdulilah Daoud

Hoang Dao-Ba

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A Kernel Theory of Modern Data Augmentation.T. Dao, A. Gu, A. Ratner, V. Smith, C. Sa, и C. Ré. ICML, том 97 из Proceedings of Machine Learning Research, стр. 1528-1537. PMLR, (2019)Learning Invariance with Compact Transforms.A. Thomas, A. Gu, T. Dao, A. Rudra, и C. Ré. ICLR (Workshop), OpenReview.net, (2018)Mamba: Linear-Time Sequence Modeling with Selective State SpacesA. Gu, и T. Dao. (2024)Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps.T. Dao, N. Sohoni, A. Gu, M. Eichhorn, A. Blonder, M. Leszczynski, A. Rudra, и C. Ré. CoRR, (2020)Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees.J. Wang, B. Yuan, L. Rimanic, Y. He, T. Dao, B. Chen, C. Ré, и C. Zhang. NeurIPS, (2022)Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers.A. Gu, I. Johnson, K. Goel, K. Saab, T. Dao, A. Rudra, и C. Ré. NeurIPS, стр. 572-585. (2021)On the Downstream Performance of Compressed Word Embeddings.A. May, J. Zhang, T. Dao, и C. Ré. NeurIPS, стр. 11782-11793. (2019)Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time.Z. Liu, J. Wang, T. Dao, T. Zhou, B. Yuan, Z. Song, A. Shrivastava, C. Zhang, Y. Tian, C. Ré и 1 other автор(ы). ICML, том 202 из Proceedings of Machine Learning Research, стр. 22137-22176. PMLR, (2023)Knowledge Distillation as Semiparametric Inference.T. Dao, G. Kamath, V. Syrgkanis, и L. Mackey. ICLR, OpenReview.net, (2021)Flashattention: Fast and memory-efficient exact attention with io-awarenessT. Dao, D. Fu, S. Ermon, A. Rudra, и C. Ré. Advances in Neural Information Processing Systems, (2022)

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