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Daniel Y. Fu
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2020 – today
- 2024
- [c15]Daniel Y. Fu, Hermann Kumbong, Eric Nguyen, Christopher Ré:
FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor Cores. ICLR 2024 - [c14]Jon Saad-Falcon, Daniel Y. Fu, Simran Arora, Neel Guha, Christopher Ré:
Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT. ICML 2024 - [i23]Jordan Juravsky, Bradley C. A. Brown, Ryan Saul Ehrlich, Daniel Y. Fu, Christopher Ré, Azalia Mirhoseini:
Hydragen: High-Throughput LLM Inference with Shared Prefixes. CoRR abs/2402.05099 (2024) - [i22]Jon Saad-Falcon, Daniel Y. Fu, Simran Arora, Neel Guha, Christopher Ré:
Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT. CoRR abs/2402.07440 (2024) - 2023
- [c13]Daniel Y. Fu, Tri Dao, Khaled Kamal Saab, Armin W. Thomas, Atri Rudra, Christopher Ré:
Hungry Hungry Hippos: Towards Language Modeling with State Space Models. ICLR 2023 - [c12]Daniel Y. Fu, Elliot L. Epstein, Eric Nguyen, Armin W. Thomas, Michael Zhang, Tri Dao, Atri Rudra, Christopher Ré:
Simple Hardware-Efficient Long Convolutions for Sequence Modeling. ICML 2023: 10373-10391 - [c11]Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y. Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, Christopher Ré:
Hyena Hierarchy: Towards Larger Convolutional Language Models. ICML 2023: 28043-28078 - [c10]Daniel Y. Fu, Simran Arora, Jessica Grogan, Isys Johnson, Evan Sabri Eyuboglu, Armin W. Thomas, Benjamin Spector, Michael Poli, Atri Rudra, Christopher Ré:
Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture. NeurIPS 2023 - [c9]Stefano Massaroli, Michael Poli, Daniel Y. Fu, Hermann Kumbong, Rom N. Parnichkun, David W. Romero, Aman Timalsina, Quinn McIntyre, Beidi Chen, Atri Rudra, Ce Zhang, Christopher Ré, Stefano Ermon, Yoshua Bengio:
Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions. NeurIPS 2023 - [i21]Daniel Y. Fu, Elliot L. Epstein, Eric Nguyen, Armin W. Thomas, Michael Zhang, Tri Dao, Atri Rudra, Christopher Ré:
Simple Hardware-Efficient Long Convolutions for Sequence Modeling. CoRR abs/2302.06646 (2023) - [i20]Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y. Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, Christopher Ré:
Hyena Hierarchy: Towards Larger Convolutional Language Models. CoRR abs/2302.10866 (2023) - [i19]Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Daniel Y. Fu, Zhiqiang Xie, Beidi Chen, Clark W. Barrett, Joseph E. Gonzalez, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang:
High-throughput Generative Inference of Large Language Models with a Single GPU. CoRR abs/2303.06865 (2023) - [i18]Daniel Y. Fu, Simran Arora, Jessica Grogan, Isys Johnson, Sabri Eyuboglu, Armin W. Thomas, Benjamin Spector, Michael Poli, Atri Rudra, Christopher Ré:
Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture. CoRR abs/2310.12109 (2023) - [i17]Stefano Massaroli, Michael Poli, Daniel Y. Fu, Hermann Kumbong, Rom N. Parnichkun, Aman Timalsina, David W. Romero
, Quinn McIntyre, Beidi Chen, Atri Rudra, Ce Zhang, Christopher Ré, Stefano Ermon, Yoshua Bengio:
Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions. CoRR abs/2310.18780 (2023) - [i16]Daniel Y. Fu, Hermann Kumbong, Eric Nguyen, Christopher Ré:
FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor Cores. CoRR abs/2311.05908 (2023) - 2022
- [c8]Megan Leszczynski, Daniel Y. Fu, Mayee F. Chen, Christopher Ré:
TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval. ACL (Findings) 2022: 2147-2166 - [c7]Mayee F. Chen, Daniel Y. Fu, Avanika Narayan, Michael Zhang, Zhao Song, Kayvon Fatahalian, Christopher Ré:
Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning. ICML 2022: 3090-3122 - [c6]Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré:
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness. NeurIPS 2022 - [c5]Mayee F. Chen, Daniel Y. Fu, Dyah Adila, Michael Zhang, Frederic Sala, Kayvon Fatahalian, Christopher Ré:
Shoring up the foundations: fusing model embeddings and weak supervision. UAI 2022: 357-367 - [i15]Mayee F. Chen, Daniel Y. Fu, Dyah Adila, Michael Zhang, Frederic Sala, Kayvon Fatahalian, Christopher Ré:
Shoring Up the Foundations: Fusing Model Embeddings and Weak Supervision. CoRR abs/2203.13270 (2022) - [i14]Mayee F. Chen, Daniel Y. Fu, Avanika Narayan, Michael Zhang, Zhao Song, Kayvon Fatahalian, Christopher Ré:
Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning. CoRR abs/2204.07596 (2022) - [i13]Megan Leszczynski, Daniel Y. Fu, Mayee F. Chen, Christopher Ré:
TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval. CoRR abs/2204.08173 (2022) - [i12]Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré:
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness. CoRR abs/2205.14135 (2022) - [i11]Trenton Chang, Daniel Y. Fu:
Lost in Transmission: On the Impact of Networking Corruptions on Video Machine Learning Models. CoRR abs/2206.05252 (2022) - [i10]Tri Dao, Daniel Y. Fu, Khaled Kamal Saab, Armin W. Thomas, Atri Rudra, Christopher Ré:
Hungry Hungry Hippos: Towards Language Modeling with State Space Models. CoRR abs/2212.14052 (2022) - 2021
- [c4]James Hong, Will Crichton
Analysis of Faces in a Decade of US Cable TV News. KDD 2021: 3011-3021 - [i9]Florian Dubost, Khaled Kamal Saab, Erin Hong, Daniel Yang Fu, Max Pike, Siddharth Sharma, Siyi Tang, Nandita Bhaskhar, Christopher Lee-Messer, Daniel L. Rubin:
Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels. CoRR abs/2106.02100 (2021) - 2020
- [c3]Daniel Y. Fu, Mayee F. Chen, Frederic Sala, Sarah M. Hooper, Kayvon Fatahalian, Christopher Ré:
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods. ICML 2020: 3280-3291 - [i8]Daniel Y. Fu, Mayee F. Chen, Frederic Sala, Sarah M. Hooper, Kayvon Fatahalian, Christopher Ré:
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods. CoRR abs/2002.11955 (2020) - [i7]Mayee F. Chen, Daniel Y. Fu, Frederic Sala, Sen Wu, Ravi Teja Mullapudi, Fait Poms, Kayvon Fatahalian, Christopher Ré:
Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings. CoRR abs/2006.15168 (2020) - [i6]James Hong, Will Crichton, Haotian Zhang, Daniel Y. Fu, Jacob Ritchie, Jeremy Barenholtz, Ben Hannel, Xinwei Yao, Michaela Murray, Geraldine Moriba, Maneesh Agrawala, Kayvon Fatahalian:
Analyzing Who and What Appears in a Decade of US Cable TV News. CoRR abs/2008.06007 (2020) - [i5]Florian Dubost, Erin Hong, Daniel Y. Fu, Nandita Bhaskhar, Siyi Tang, Khaled Saab, Daniel L. Rubin, Jared Dunnmon, Christopher Lee-Messer:
Let's Hope it Works! Inaccurate Supervision of Neural Networks with Incorrect Labels: Application to Epilepsy. CoRR abs/2011.14101 (2020)
2010 – 2019
- 2019
- [c2]Paroma Varma, Frederic Sala, Shiori Sagawa, Jason Alan Fries, Daniel Y. Fu, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, Christopher Ré:
Multi-Resolution Weak Supervision for Sequential Data. NeurIPS 2019: 192-203 - [i4]Daniel Y. Fu, Will Crichton, James Hong, Xinwei Yao, Haotian Zhang, Anh Truong, Avanika Narayan, Maneesh Agrawala, Christopher Ré, Kayvon Fatahalian:
Rekall: Specifying Video Events using Compositions of Spatiotemporal Labels. CoRR abs/1910.02993 (2019) - [i3]Frederic Sala, Paroma Varma, Jason A. Fries, Daniel Y. Fu, Shiori Sagawa, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, Christopher Ré:
Multi-Resolution Weak Supervision for Sequential Data. CoRR abs/1910.09505 (2019) - 2018
- [c1]Daniel Y. Fu, Emily S. Wang, Peter M. Krafft, Barbara J. Grosz:
Influencing Flock Formation in Low-Density Settings. AAMAS 2018: 1613-1621 - [i2]Daniel Y. Fu
Influencing Flock Formation in Low-Density Settings. CoRR abs/1804.08667 (2018) - [i1]Peter Kraft, Amos Waterland, Daniel Y. Fu, Anitha Gollamudi, Shai Szulanski, Margo I. Seltzer:
Automatic Parallelization of Sequential Programs. CoRR abs/1809.07684 (2018)
Coauthor Index
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last updated on 2024-10-07 22:06 CEST by the dblp team
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