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Samuel L. Smith
Samuel L. Smith
DeepMind
Bestätigte E-Mail-Adresse bei google.com - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Don't Decay the Learning Rate, Increase the Batch Size
SL Smith, PJ Kindermans, C Ying, QV Le
International Conference on Learning Representations, 2018
11922018
Ultrafast long-range charge separation in organic semiconductor photovoltaic diodes
S Gélinas, A Rao, A Kumar, SL Smith, AW Chin, J Clark, TS van der Poll, ...
Science 343 (6170), 512-516, 2014
10122014
Offline bilingual word vectors, orthogonal transformations and the inverted softmax
SL Smith, DHP Turban, S Hamblin, NY Hammerla
International Conference on Learning Representations, 2017
5992017
High-performance large-scale image recognition without normalization
A Brock, S De, SL Smith, K Simonyan
International Conference on Machine Learning, 1059-1071, 2021
4882021
A Bayesian Perspective on Generalization and Stochastic Gradient Descent
SL Smith, QV Le
International Conference on Learning Representations, 2018
3882018
The future of quantum biology
A Marais, B Adams, AK Ringsmuth, M Ferretti, JM Gruber, R Hendrikx, ...
Journal of the Royal Society Interface 15 (148), 20180640, 2018
2222018
On the Origin of Implicit Regularization in Stochastic Gradient Descent
SL Smith, B Dherin, DGT Barrett, S De
arXiv preprint arXiv:2101.12176, 2021
1772021
Batch normalization biases residual blocks towards the identity function in deep networks
S De, S Smith
Advances in Neural Information Processing Systems 33, 19964-19975, 2020
151*2020
Unlocking high-accuracy differentially private image classification through scale
S De, L Berrada, J Hayes, SL Smith, B Balle
arXiv preprint arXiv:2204.13650, 2022
1392022
Characterizing signal propagation to close the performance gap in unnormalized ResNets
A Brock, S De, SL Smith
arXiv preprint arXiv:2101.08692, 2021
1192021
Resurrecting recurrent neural networks for long sequences
A Orvieto, SL Smith, A Gu, A Fernando, C Gulcehre, R Pascanu, S De
International Conference on Machine Learning, 26670-26698, 2023
1042023
On the Generalization Benefit of Noise in Stochastic Gradient Descent
S Smith, E Elsen, S De
International Conference on Machine Learning, 9058-9067, 2020
942020
BYOL works even without batch statistics
PH Richemond, JB Grill, F Altché, C Tallec, F Strub, A Brock, S Smith, ...
arXiv preprint arXiv:2010.10241, 2020
852020
Gemma: Open Models Based on Gemini Research and Technology
G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ...
arXiv preprint arXiv:2403.08295, 2024
552024
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
DS Park, J Sohl-Dickstein, QV Le, SL Smith
International Conference on Machine Learning, 2019
552019
Phonon-assisted ultrafast charge separation in the PCBM band structure
SL Smith, AW Chin
Physical Review B 91 (20), 201302, 2015
412015
Ultrafast charge separation and nongeminate electron–hole recombination in organic photovoltaics
SL Smith, AW Chin
Physical Chemistry Chemical Physics 16 (38), 20305-20309, 2014
412014
Differentially Private Diffusion Models Generate Useful Synthetic Images
S Ghalebikesabi, L Berrada, S Gowal, I Ktena, R Stanforth, J Hayes, S De, ...
arXiv preprint arXiv:2302.13861, 2023
312023
Drawing multiple augmentation samples per image during training efficiently decreases test error
S Fort, A Brock, R Pascanu, S De, SL Smith
arXiv preprint arXiv:2105.13343, 2021
252021
Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation
B He, J Martens, G Zhang, A Botev, A Brock, SL Smith, YW Teh
arXiv preprint arXiv:2302.10322, 2023
182023
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