| Training quantized nets: A deeper understanding H Li, S De, Z Xu, C Studer, H Samet, T Goldstein Advances in Neural Information Processing Systems, 5813-5823, 2017 | 113 | 2017 |
| Automated inference with adaptive batches S De, A Yadav, D Jacobs, T Goldstein Artificial Intelligence and Statistics, 1504-1513, 2017 | 86* | 2017 |
| Adversarial robustness through local linearization C Qin, J Martens, S Gowal, D Krishnan, K Dvijotham, A Fawzi, S De, ... Advances in Neural Information Processing Systems, 13847-13856, 2019 | 84 | 2019 |
| Convergence guarantees for rmsprop and adam in non-convex optimization and their comparison to nesterov acceleration on autoencoders A Basu, S De, A Mukherjee, E Ullah arXiv preprint arXiv:1807.06766, 2018 | 50* | 2018 |
| Efficient distributed SGD with variance reduction S De, T Goldstein 2016 IEEE International Conference on Data Mining (ICDM), 2016 | 39* | 2016 |
| Understanding norm change: An evolutionary game-theoretic approach S De, DS Nau, MJ Gelfand Proceedings of the 16th Conference on Autonomous Agents and MultiAgent …, 2017 | 38* | 2017 |
| The loosening of American culture over 200 years is associated with a creativity–order trade-off JC Jackson, M Gelfand, S De, A Fox Nature human behaviour 3 (3), 244-250, 2019 | 37 | 2019 |
| The impact of neural network overparameterization on gradient confusion and stochastic gradient descent KA Sankararaman, S De, Z Xu, WR Huang, T Goldstein International Conference on Machine Learning, 8469-8479, 2020 | 35* | 2020 |
| Layer-specific adaptive learning rates for deep networks B Singh, S De, Y Zhang, T Goldstein, G Taylor 2015 IEEE 14th International Conference on Machine Learning and Applications …, 2015 | 35* | 2015 |
| An Empirical Study of ADMM for Nonconvex Problems Z Xu, S De, M Figueiredo, C Studer, T Goldstein NIPS 2016 Workshop on Nonconvex Optimization for Machine Learning: Theory …, 2016 | 28* | 2016 |
| The Inevitability of Ethnocentrism Revisited: Ethnocentrism Diminishes As Mobility Increases S De, MJ Gelfand, D Nau, P Roos Scientific reports 5, 2015 | 21 | 2015 |
| Batch normalization biases residual blocks towards the identity function in deep networks S De, S Smith Advances in Neural Information Processing Systems 33, 2020 | 19* | 2020 |
| Efficient neural network verification with exactness characterization KD Dvijotham, R Stanforth, S Gowal, C Qin, S De, P Kohli Uncertainty in Artificial Intelligence, 497-507, 2019 | 14* | 2019 |
| iAnon: Leveraging social network big data to mitigate behavioral symptoms of cyberbullying Z Ashktorab, S Kumar, S De, J Golbeck iConference 2014 (Social Media Expo), 2014 | 10* | 2014 |
| resMBS: Constructing a financial supply chain graph from financial prospecti D Burdick, S De, L Raschid, M Shao, Z Xu, E Zotkina SIGMOD DSMM. ACM, 2016 | 6* | 2016 |
| On the Generalization Benefit of Noise in Stochastic Gradient Descent S Smith, E Elsen, S De International Conference on Machine Learning, 9058-9067, 2020 | 4* | 2020 |
| Characterizing signal propagation to close the performance gap in unnormalized ResNets A Brock, S De, SL Smith arXiv preprint arXiv:2101.08692, 2021 | 2 | 2021 |
| 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 | 2 | 2020 |
| Visibility of Nodes in Network Growth Models S Pal, S De, T Chakraborty, R Gera International Conference and School on Network Science, 35-45, 2017 | 2 | 2017 |
| Plagiarism Detection in Polyphonic Music using Monaural Signal Separation S De, I Roy, T Prabhakar, K Suneja, S Chaudhuri, R Singh, B Raj In Thirteenth Annual Conference of the International Speech Communication …, 2012 | 2 | 2012 |
Tom GoldsteinPerotto Associate Professor of Computer Science, University of MarylandVerified email at cs.umd.edu
