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Albert Gu
Albert Gu
Verified email at andrew.cmu.edu
Title
Cited by
Cited by
Year
Efficiently modeling long sequences with structured state spaces
A Gu, K Goel, C Ré
arXiv preprint arXiv:2111.00396, 2021
7752021
Mamba: Linear-time sequence modeling with selective state spaces
A Gu, T Dao
arXiv preprint arXiv:2312.00752, 2023
5542023
Representation tradeoffs for hyperbolic embeddings
F Sala, C De Sa, A Gu, C Ré
International conference on machine learning, 4460-4469, 2018
4382018
Hippo: Recurrent memory with optimal polynomial projections
A Gu, T Dao, S Ermon, A Rudra, C Ré
Advances in neural information processing systems 33, 1474-1487, 2020
2612020
Combining recurrent, convolutional, and continuous-time models with linear state space layers
A Gu, I Johnson, K Goel, K Saab, T Dao, A Rudra, C Ré
Advances in neural information processing systems 34, 572-585, 2021
2512021
Learning mixed-curvature representations in products of model spaces
A Gu, F Sala, B Gunel, C Ré
International conference on learning representations 5, 2019
2322019
No subclass left behind: Fine-grained robustness in coarse-grained classification problems
N Sohoni, J Dunnmon, G Angus, A Gu, C Ré
Advances in Neural Information Processing Systems 33, 19339-19352, 2020
2062020
A kernel theory of modern data augmentation
T Dao, A Gu, A Ratner, V Smith, C De Sa, C Ré
International conference on machine learning, 1528-1537, 2019
2052019
On the parameterization and initialization of diagonal state space models
A Gu, K Goel, A Gupta, C Ré
Advances in Neural Information Processing Systems 35, 35971-35983, 2022
1692022
Diagonal state spaces are as effective as structured state spaces
A Gupta, A Gu, J Berant
Advances in Neural Information Processing Systems 35, 22982-22994, 2022
1622022
It’s raw! audio generation with state-space models
K Goel, A Gu, C Donahue, C Ré
International Conference on Machine Learning, 7616-7633, 2022
1482022
Resurrecting recurrent neural networks for long sequences
A Orvieto, SL Smith, A Gu, A Fernando, C Gulcehre, R Pascanu, S De
International Conference on Machine Learning, 26670-26698, 2023
1462023
S4nd: Modeling images and videos as multidimensional signals with state spaces
E Nguyen, K Goel, A Gu, G Downs, P Shah, T Dao, S Baccus, C Ré
Advances in neural information processing systems 35, 2846-2861, 2022
1102022
Learning fast algorithms for linear transforms using butterfly factorizations
T Dao, A Gu, M Eichhorn, A Rudra, C Ré
International conference on machine learning, 1517-1527, 2019
1062019
From trees to continuous embeddings and back: Hyperbolic hierarchical clustering
I Chami, A Gu, V Chatziafratis, C Ré
Advances in Neural Information Processing Systems 33, 15065-15076, 2020
972020
Model patching: Closing the subgroup performance gap with data augmentation
K Goel, A Gu, Y Li, C Ré
arXiv preprint arXiv:2008.06775, 2020
732020
Improving the gating mechanism of recurrent neural networks
A Gu, C Gulcehre, T Paine, M Hoffman, R Pascanu
International conference on machine learning, 3800-3809, 2020
702020
The power of deferral: maintaining a constant-competitive steiner tree online
A Gu, A Gupta, A Kumar
Proceedings of the forty-fifth annual ACM symposium on Theory of Computing …, 2013
682013
How to train your hippo: State space models with generalized orthogonal basis projections
A Gu, I Johnson, A Timalsina, A Rudra, C Ré
arXiv preprint arXiv:2206.12037, 2022
582022
Kaleidoscope: An efficient, learnable representation for all structured linear maps
T Dao, NS Sohoni, A Gu, M Eichhorn, A Blonder, M Leszczynski, A Rudra, ...
arXiv preprint arXiv:2012.14966, 2020
512020
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Articles 1–20