| On the optimal weighted regularization in overparameterized linear regression D Wu, J Xu Advances in Neural Information Processing Systems 33, 10112-10123, 2020 | 182 | 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 | 176 | 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 | 159 | 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 | 107 | 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 | 95 | 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 | 75 | 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 | 22 | 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 Tokyo在 mist.i.u-tokyo.ac.jp 的電子郵件地址已通過驗證