On the optimal weighted D Wu, J Xu
Advances in Neural Information Processing Systems 33, 10112-10123, 2020
181 2020 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
174 2022 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
157 2018 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
106 2017 Generalization of two-layer neural networks: An asymptotic viewpoint J Ba, M Erdogdu, T Suzuki, D Wu, T Zhang
International conference on learning representations, 2020
94 2020 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
83 2019 Convex analysis of the mean-field Langevin dynamics A Nitanda, D Wu, T Suzuki
arXiv preprint arXiv:2201.10469, 2022
76 2022 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
62 2019 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
49 2021 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
44 2018 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
43 * 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
36 2021 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
35 2017 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, 17420-17449, 2023
34 2023 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
29 * 2019 Gradient-based feature learning under structured data A Mousavi-Hosseini, D Wu, T Suzuki, MA Erdogdu
Advances in Neural Information Processing Systems 36, 71449-71485, 2023
26 2023 Neural network learns low-dimensional polynomials with sgd near the information-theoretic limit JD Lee, K Oko, T Suzuki, D Wu
Advances in Neural Information Processing Systems 37, 58716-58756, 2024
24 * 2024 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
24 2023 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
20 2016 Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics T Suzuki, A Nitanda, D Wu
The Eleventh International Conference on Learning Representations, 2023
17 2023 
Taiji SuzukiThe University of TokyoПотвърден имейл адрес: mist.i.u-tokyo.ac.jp