Generalizable adversarial training via spectral normalization F Farnia, JM Zhang, D Tse arXiv preprint arXiv:1811.07457, 2018 | 176 | 2018 |
Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts V Ntranos, GM Kamath, JM Zhang, L Pachter, DN Tse Genome biology 17 (1), 112, 2016 | 158 | 2016 |
Lysine-specific demethylase 1 has dual functions as a major regulator of androgen receptor transcriptional activity C Cai, HH He, S Gao, S Chen, Z Yu, Y Gao, S Chen, MW Chen, J Zhang, ... Cell reports 9 (5), 1618-1627, 2014 | 151 | 2014 |
Valid post-clustering differential analysis for single-cell RNA-Seq JM Zhang, GM Kamath, NT David Cell systems 9 (4), 383-392. e6, 2019 | 76 | 2019 |
An interpretable framework for clustering single-cell RNA-Seq datasets JM Zhang, J Fan, HC Fan, D Rosenfeld, DN Tse BMC bioinformatics 19, 1-12, 2018 | 54 | 2018 |
Porcupine neural networks:(almost) all local optima are global S Feizi, H Javadi, J Zhang, D Tse arXiv preprint arXiv:1710.02196, 2017 | 39 | 2017 |
Prediction of price increase for magic: The gathering cards M Pawlicki, J Polin, J Zhang Class report, Stanford University, 2014 | 11 | 2014 |
A Fourier-based approach to generalization and optimization in deep learning F Farnia, JM Zhang, NT David IEEE Journal on Selected Areas in Information Theory 1 (1), 145-156, 2020 | 6 | 2020 |
Porcupine neural networks: Approximating neural network landscapes S Feizi, H Javadi, J Zhang, D Tse Advances in Neural Information Processing Systems 31, 2018 | 6 | 2018 |
Towards a Post-Clustering Test for Differential Expression. JM Zhang, GM Kamath, NC David RECOMB, 328-329, 2019 | 5 | 2019 |
A spectral approach to generalization and optimization in neural networks F Farnia, J Zhang, D Tse | 4 | 2018 |
Learning the language of the genome using RNNs JM Zhang, GM Kamath Go to reference in article, 2016 | 2 | 2016 |