フォロー
Denny Wu
Denny Wu
確認したメール アドレス: nyu.edu - ホームページ
タイトル
引用先
引用先
High-throughput imaging flow cytometry by optofluidic time-stretch microscopy
C Lei, H Kobayashi, D Wu, M Li, A Isozaki, A Yasumoto, H Mikami, T Ito, ...
Nature protocols 13 (7), 1603-1631, 2018
1312018
On the optimal weighted regularization in overparameterized linear regression
D Wu, J Xu
Advances in Neural Information Processing Systems 33, 10112-10123, 2020
1282020
Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning
H Kobayashi, C Lei, D Wu, A Mao, Y Jiang, B Guo, Y Ozeki, K Goda
Scientific reports 7 (1), 1-9, 2017
902017
Generalization of two-layer neural networks: an asymptotic viewpoint
J Ba, MA Erdogdu, T Suzuki, D Wu, T Zhang
International Conference on Learning Representations 8, 2020
792020
High-dimensional asymptotics of feature learning: how one gradient step improves the representation
J Ba, MA Erdogdu, T Suzuki, Z Wang, D Wu, G Yang
arXiv preprint arXiv:2205.01445, 2022
752022
Stochastic Runge-Kutta accelerates Langevin Monte Carlo and beyond
X Li, D Wu, L Mackey, MA Erdogdu
Advances in Neural Information Processing Systems, 7748-7760, 2019
692019
Intelligent whole-blood imaging flow cytometry for simple, rapid, and cost-effective drug-susceptibility testing of leukemia
H Kobayashi, C Lei, D Wu, CJ Huang, A Yasumoto, M Jona, W Li, Y Wu, ...
Lab on a Chip 19 (16), 2688-2698, 2019
522019
Convex analysis of the mean-field Langevin dynamics
A Nitanda, D Wu, T Suzuki
arXiv preprint arXiv:2201.10469, 2022
452022
Optofluidic time-stretch quantitative phase microscopy
B Guo, C Lei, D Wu, H Kobayashi, T Ito, Y Yalikun, S Lee, A Isozaki, M Li, ...
Methods 136, 116-125, 2018
382018
When does preconditioning help or hurt generalization?
S Amari, J Ba, R Grosse, X Li, A Nitanda, T Suzuki, D Wu, J Xu
International Conference on Learning Representations 9, 2021
362021
GHz optical time-stretch microscopy by compressive sensing
C Lei, D Wu, AC Sankaranarayanan, SM Chang, B Guo, N Sasaki, ...
IEEE Photonics Journal 9 (2), 1-8, 2017
312017
Understanding the variance collapse of SVGD in high dimensions
J Ba, MA Erdogdu, M Ghassemi, S Sun, T Suzuki, D Wu, T Zhang
International Conference on Learning Representations 10, 2022
27*2022
Particle dual averaging: optimization of mean field neural network with global convergence rate analysis
A Nitanda, D Wu, T Suzuki
Advances in Neural Information Processing Systems 34, 19608-19621, 2021
252021
Post selection inference with incomplete maximum mean discrepancy estimator
M Yamada, D Wu, YHH Tsai, I Takeuchi, R Salakhutdinov, K Fukumizu
International Conference on Learning Representations 7, 2019
22*2019
Light scattering analysis of mono-and multi-pegylated bovine serum albumin in solution: role of composition on structure and interactions
R Ferebee, IF Hakem, A Koch, M Chen, D Wu, D Loh, DC Wilson, ...
The Journal of Physical Chemistry B 120 (20), 4591-4599, 2016
192016
Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics
T Suzuki, A Nitanda, D Wu
International Conference on Learning Representations 11, 2023
112023
Learning in the presence of low-dimensional structure: a spiked random matrix perspective
J Ba, MA Erdogdu, T Suzuki, Z Wang, D Wu
Advances in Neural Information Processing Systems 36, 2023
102023
Particle stochastic dual coordinate ascent: exponential convergent algorithm for mean-field neural network optimization
K Oko, T Suzuki, A Nitanda, D Wu
International Conference on Learning Representations 10, 2022
92022
Mean-field Langevin dynamics: Time-space discretization, stochastic gradient, and variance reduction
T Suzuki, D Wu, A Nitanda
Advances in Neural Information Processing Systems 36, 2023
7*2023
Gradient-based feature learning under structured data
A Mousavi-Hosseini, D Wu, T Suzuki, MA Erdogdu
Advances in Neural Information Processing Systems 36, 2023
62023
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論文 1–20