ติดตาม
Hadi Daneshmand
Hadi Daneshmand
Assistant Professor of Computer Science at University of Virginia
ยืนยันอีเมลแล้วที่ virginia.edu - หน้าแรก
ชื่อ
อ้างโดย
อ้างโดย
ปี
Inferring causal molecular networks: empirical assessment through a community-based effort
SM Hill, LM Heiser, T Cokelaer, M Unger, NK Nesser, DE Carlin, Y Zhang, ...
Nature methods 13 (4), 310-318, 2016
2632016
Transformers learn to implement preconditioned gradient descent for in-context learning
K Ahn, X Cheng, H Daneshmand, S Sra
Advances in Neural Information Processing Systems 36, 45614-45650, 2023
2112023
Escaping saddles with stochastic gradients
H Daneshmand, J Kohler, A Lucchi, T Hofmann
International Conference on Machine Learning, 1155-1164, 2018
1802018
Exponential convergence rates for batch normalization: The power of length-direction decoupling in non-convex optimization
J Kohler, H Daneshmand, A Lucchi, T Hofmann, M Zhou, K Neymeyr
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
159*2019
Estimating diffusion network structures: Recovery conditions, sample complexity & soft-thresholding algorithm
H Daneshmand, M Gomez-Rodriguez, L Song, B Schoelkopf
International conference on machine learning, 793-801, 2014
143*2014
Local saddle point optimization: A curvature exploitation approach
L Adolphs, H Daneshmand, A Lucchi, T Hofmann
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
1382019
Batch normalization provably avoids ranks collapse for randomly initialised deep networks
H Daneshmand, J Kohler, F Bach, T Hofmann, A Lucchi
Advances in Neural Information Processing Systems 33, 18387-18398, 2020
712020
Adaptive newton method for empirical risk minimization to statistical accuracy
A Mokhtari, H Daneshmand, A Lucchi, T Hofmann, A Ribeiro
Advances in Neural Information Processing Systems 29, 2016
522016
Starting small-learning with adaptive sample sizes
H Daneshmand, A Lucchi, T Hofmann
International conference on machine learning, 1463-1471, 2016
512016
Batch normalization orthogonalizes representations in deep random networks
H Daneshmand, A Joudaki, F Bach
Advances in Neural Information Processing Systems 34, 4896-4906, 2021
452021
Estimating diffusion networks: Recovery conditions, sample complexity and soft-thresholding algorithm
M Gomez-Rodriguez, L Song, H Daneshm, B Schölkopf
Journal of Machine Learning Research 17 (90), 1-29, 2016
382016
A time-aware recommender system based on dependency network of items
SM Daneshmand, A Javari, SE Abtahi, M Jalili
The Computer Journal 58 (9), 1955-1966, 2015
252015
On the impact of activation and normalization in obtaining isometric embeddings at initialization
A Joudaki, H Daneshmand, F Bach
Advances in Neural Information Processing Systems 36, 39855-39875, 2023
142023
Revisiting the role of euler numerical integration on acceleration and stability in convex optimization
P Zhang, A Orvieto, H Daneshmand, T Hofmann, RS Smith
International Conference on Artificial Intelligence and Statistics, 3979-3987, 2021
112021
Transformers learn temporal difference methods for in-context reinforcement learning
J Wang, E Blaser, H Daneshmand, S Zhang
arXiv preprint arXiv:2405.13861, 2024
72024
Towards training without depth limits: Batch normalization without gradient explosion
A Meterez, A Joudaki, F Orabona, A Immer, G Rätsch, H Daneshmand
arXiv preprint arXiv:2310.02012, 2023
62023
On bridging the gap between mean field and finite width deep random multilayer perceptron with batch normalization
A Joudaki, H Daneshmand, F Bach
International Conference on Machine Learning, 15388-15400, 2023
5*2023
Rethinking the variational interpretation of accelerated optimization methods
P Zhang, A Orvieto, H Daneshmand
Advances in Neural Information Processing Systems 34, 14396-14406, 2021
5*2021
Efficient displacement convex optimization with particle gradient descent
H Daneshmand, JD Lee, C Jin
International Conference on Machine Learning, 6836-6854, 2023
42023
Polynomial-time sparse measure recovery
H Daneshmand, F Bach
arXiv preprint arXiv:2204.07879, 2022
32022
ระบบไม่สามารถดำเนินการได้ในขณะนี้ โปรดลองใหม่อีกครั้งในภายหลัง
บทความ 1–20