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Shuai Tang
Shuai Tang
Quant Research @ Jump Trading
Keine bestätigte E-Mail-Adresse - Startseite
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Zitiert von
Zitiert von
Jahr
Image captioning utilizing semantic text modeling and adversarial learning
Z Wang, T Shuai, H Jin, C Fang
US Patent App. 15/630,604, 2018
472018
Fast adaptation with linearized neural networks
W Maddox, S Tang, P Moreno, AG Wilson, A Damianou
International Conference on Artificial Intelligence and Statistics, 2737-2745, 2021
452021
Scalable membership inference attacks via quantile regression
M Bertran, S Tang, A Roth, M Kearns, JH Morgenstern, SZ Wu
Advances in Neural Information Processing Systems 36, 2024
332024
Private synthetic data for multitask learning and marginal queries
G Vietri, C Archambeau, S Aydore, W Brown, M Kearns, A Roth, A Siva, ...
Advances in Neural Information Processing Systems 35, 18282-18295, 2022
242022
Similarity of neural networks with gradients
S Tang, WJ Maddox, C Dickens, T Diethe, A Damianou
arXiv preprint arXiv:2003.11498, 2020
202020
Rethinking skip-thought: A neighborhood based approach
S Tang, H Jin, C Fang, Z Wang, VR de Sa
2nd Workshop on Representation Learning for Natural Language Processing at …, 2017
192017
Speeding up context-based sentence representation learning with non-autoregressive convolutional decoding
S Tang, H Jin, C Fang, Z Wang, VR de Sa
3nd Workshop on Representation Learning for Natural Language Processing at …, 2018
17*2018
Improving Sentence Representations with Consensus Maximisation
S Tang, VR de Sa
Workshop on interpretability and robustness in audio, speech, and language …, 2018
12*2018
Trimming and improving skip-thought vectors
S Tang, H Jin, C Fang, Z Wang, VR de Sa
arXiv preprint arXiv:1706.03148, 2017
82017
Membership inference attacks on diffusion models via quantile regression
S Tang, ZS Wu, S Aydore, M Kearns, A Roth
arXiv preprint arXiv:2312.05140, 2023
7*2023
An Empirical Study on Post-processing Methods for Word Embeddings
S Tang, M Mousavi, VR de Sa
arXiv preprint arXiv:1905.10971v2, 2019
72019
What happened to my dog in that network: Unraveling top-down generators in convolutional neural networks
PW Gallagher, S Tang, Z Tu
arXiv preprint arXiv:1511.07125, 2015
52015
Improved differentially private regression via gradient boosting
S Tang, S Aydore, M Kearns, S Rho, A Roth, Y Wang, YX Wang, ZS Wu
2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 33-56, 2024
42024
A Simple Recurrent Unit with Reduced Tensor Product Representations
S Tang, P Smolensky, VR de Sa
arXiv preprint arXiv:1810.12456v5, 2018
4*2018
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable
M Bertran, S Tang, M Kearns, J Morgenstern, A Roth, ZS Wu
arXiv preprint arXiv:2405.20272, 2024
32024
Spectrally adaptive common spatial patterns
M Mousavi, E Lybrand, S Feng, S Tang, R Saab, V de Sa
arXiv preprint arXiv:2202.04542, 2022
32022
Deep transfer learning with ridge regression
S Tang, VR de Sa
arXiv preprint arXiv:2006.06791, 2020
22020
Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning
S Tang, VR de Sa
Proceedings of the 57th Conference of the Association for Computational …, 2019
22019
Supervised Spike Sorting Using Deep Convolutional Siamese Network and Hierarchical Clustering
Y Li, S Tang, VR de Sa
22019
Improving robustness in motor imagery brain-computer interfaces
M Mousavi, E Lybrand, S Feng, S Tang, R Saab, VR de Sa
NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and …, 2021
12021
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