Daniel Y Fu
Daniel Y Fu
Graduate Student, Stanford University
Verified email at - Homepage
Cited by
Cited by
Flashattention: Fast and memory-efficient exact attention with io-awareness
T Dao, D Fu, S Ermon, A Rudra, C Ré
Advances in Neural Information Processing Systems 35, 16344-16359, 2022
Hungry hungry hippos: Towards language modeling with state space models
DY Fu, T Dao, KK Saab, AW Thomas, A Rudra, C Ré
The Eleventh International Conference on Learning Representations, 2023
Hyena hierarchy: Towards larger convolutional language models
M Poli, S Massaroli, E Nguyen, DY Fu, T Dao, S Baccus, Y Bengio, ...
International Conference on Machine Learning, 28043-28078, 2023
Flexgen: High-throughput generative inference of large language models with a single gpu
Y Sheng, L Zheng, B Yuan, Z Li, M Ryabinin, B Chen, P Liang, C Ré, ...
International Conference on Machine Learning, 31094-31116, 2023
Fast and three-rious: Speeding up weak supervision with triplet methods
D Fu, M Chen, F Sala, S Hooper, K Fatahalian, C Ré
International conference on machine learning, 3280-3291, 2020
Rekall: Specifying video events using compositions of spatiotemporal labels
DY Fu, W Crichton, J Hong, X Yao, H Zhang, A Truong, A Narayan, ...
arXiv preprint arXiv:1910.02993, 2019
Perfectly balanced: Improving transfer and robustness of supervised contrastive learning
M Chen, DY Fu, A Narayan, M Zhang, Z Song, K Fatahalian, C Ré
International Conference on Machine Learning, 3090-3122, 2022
Simple hardware-efficient long convolutions for sequence modeling
DY Fu, EL Epstein, E Nguyen, AW Thomas, M Zhang, T Dao, A Rudra, ...
International Conference on Machine Learning, 10373-10391, 2023
Multi-resolution weak supervision for sequential data
P Varma, F Sala, S Sagawa, J Fries, D Fu, S Khattar, A Ramamoorthy, ...
Advances in Neural Information Processing Systems 32, 2019
Shoring up the foundations: Fusing model embeddings and weak supervision
MF Chen, DY Fu, D Adila, M Zhang, F Sala, K Fatahalian, C Ré
Uncertainty in Artificial Intelligence, 357-367, 2022
Monarch mixer: A simple sub-quadratic gemm-based architecture
D Fu, S Arora, J Grogan, I Johnson, ES Eyuboglu, A Thomas, B Spector, ...
Advances in Neural Information Processing Systems 36, 2024
Analysis of faces in a decade of us cable tv news
J Hong, W Crichton, H Zhang, DY Fu, J Ritchie, J Barenholtz, B Hannel, ...
KDD'21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery …, 2021
Laughing hyena distillery: Extracting compact recurrences from convolutions
S Massaroli, M Poli, D Fu, H Kumbong, R Parnichkun, D Romero, ...
Advances in Neural Information Processing Systems 36, 2024
Tabi: Type-aware bi-encoders for open-domain entity retrieval
M Leszczynski, DY Fu, MF Chen, C Ré
arXiv preprint arXiv:2204.08173, 2022
Orexinergic neurotransmission in temperature responses to methamphetamine and stress: mathematical modeling as a data assimilation approach
A Behrouzvaziri, D Fu, P Tan, Y Yoo, MV Zaretskaia, DE Rusyniak, ...
PLoS One 10 (5), e0126719, 2015
Flashfftconv: Efficient convolutions for long sequences with tensor cores
DY Fu, H Kumbong, E Nguyen, C Ré
arXiv preprint arXiv:2311.05908, 2023
Benchmarking and building long-context retrieval models with loco and m2-bert
J Saad-Falcon, DY Fu, S Arora, N Guha, C Ré
arXiv preprint arXiv:2402.07440, 2024
Hydragen: High-Throughput LLM Inference with Shared Prefixes
J Juravsky, B Brown, R Ehrlich, DY Fu, C Ré, A Mirhoseini
arXiv preprint arXiv:2402.05099, 2024
Automatic parallelization of sequential programs
P Kraft, A Waterland, DY Fu, A Gollamudi, S Szulanski, M Seltzer
arXiv preprint arXiv:1809.07684, 2018
Chaos and robustness in a single family of genetic oscillatory networks
D Fu, P Tan, A Kuznetsov, YI Molkov
PloS one 9 (3), e90666, 2014
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