| Natural tts synthesis by conditioning wavenet on mel spectrogram predictions J Shen, R Pang, RJ Weiss, M Schuster, N Jaitly, Z Yang, Z Chen, Y Zhang, ... 2018 IEEE international conference on acoustics, speech and signal …, 2018 | 3831 | 2018 |
| CNN architectures for large-scale audio classification S Hershey, S Chaudhuri, DPW Ellis, JF Gemmeke, A Jansen, RC Moore, ... 2017 ieee international conference on acoustics, speech and signal …, 2017 | 3711 | 2017 |
| Prediction of central nervous system embryonal tumour outcome based on gene expression SL Pomeroy, P Tamayo, M Gaasenbeek, LM Sturla, M Angelo, ... Nature 415 (6870), 436-442, 2002 | 2913 | 2002 |
| Tacotron: Towards end-to-end speech synthesis Y Wang, RJ Skerry-Ryan, D Stanton, Y Wu, RJ Weiss, N Jaitly, Z Yang, ... arXiv preprint arXiv:1703.10135, 2017 | 2867* | 2017 |
| In defense of one-vs-all classification R Rifkin, A Klautau Journal of machine learning research 5 (Jan), 101-141, 2004 | 2674 | 2004 |
| Multiclass cancer diagnosis using tumor gene expression signatures S Ramaswamy, P Tamayo, R Rifkin, S Mukherjee, CH Yeang, M Angelo, ... Proceedings of the National Academy of Sciences 98 (26), 15149-15154, 2001 | 2642 | 2001 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... Transactions on machine learning research, 2023 | 2017 | 2023 |
| Style tokens: Unsupervised style modeling, control and transfer in end-to-end speech synthesis Y Wang, D Stanton, Y Zhang, RJS Ryan, E Battenberg, J Shor, Y Xiao, ... International conference on machine learning, 5180-5189, 2018 | 1113 | 2018 |
| Towards end-to-end prosody transfer for expressive speech synthesis with tacotron RJ Skerry-Ryan, E Battenberg, Y Xiao, Y Wang, D Stanton, J Shor, ... international conference on machine learning, 4693-4702, 2018 | 773 | 2018 |
| Tensorflow distributions JV Dillon, I Langmore, D Tran, E Brevdo, S Vasudevan, D Moore, B Patton, ... arXiv preprint arXiv:1711.10604, 2017 | 694 | 2017 |
| Fixing a broken ELBO A Alemi, B Poole, I Fischer, J Dillon, RA Saurous, K Murphy International conference on machine learning, 159-168, 2018 | 674 | 2018 |
| Regularized Least-Squares R Rifkin, G Yeo, T Poggio Advances in Learning Theory: Methods, Models, and Applications 190, 131, 2003 | 549 | 2003 |
| Voicefilter: Targeted voice separation by speaker-conditioned spectrogram masking Q Wang, H Muckenhirn, K Wilson, P Sridhar, Z Wu, J Hershey, ... arXiv preprint arXiv:1810.04826, 2018 | 499 | 2018 |
| Molecular classification of multiple tumor types CH Yeang, S Ramaswamy, P Tamayo, S Mukherjee, RM Rifkin, M Angelo, ... BIOINFORMATICS-OXFORD- 17, S316-S322, 2001 | 400 | 2001 |
| General conditions for predictivity in learning theory T Poggio, R Rifkin, S Mukherjee, P Niyogi Nature 428 (6981), 419-422, 2004 | 389 | 2004 |
| Estimating dataset size requirements for classifying DNA microarray data S Mukherjee, P Tamayo, S Rogers, R Rifkin, A Engle, C Campbell, ... Journal of computational biology 10 (2), 119-142, 2003 | 381 | 2003 |
| Everything old is new again: a fresh look at historical approaches in machine learning RM Rifkin MaSSachuSettS InStitute of Technology, 2002 | 324 | 2002 |
| Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization S Mukherjee, P Niyogi, T Poggio, R Rifkin Advances in Computational Mathematics 25 (1), 161-193, 2006 | 297 | 2006 |
| Deep probabilistic programming D Tran, MD Hoffman, RA Saurous, E Brevdo, K Murphy, DM Blei arXiv preprint arXiv:1701.03757, 2017 | 259 | 2017 |
| Notes on regularized least squares RM Rifkin, RA Lippert | 230 | 2007 |
Ron J WeissGoogleVerified email at google.com
