| VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning A Srivastava, L Valkov, C Russell, M Gutmann, C Sutton 31st Conference on Neural Information Processing Systems (NIPS 2017), Long …, 2017 | 234 | 2017 |
| Autoencoding Variational Inference for Topic Models A Srivastava, C Sutton International Conference on Learning Representations (ICLR), 2017 | 154 | 2017 |
| Fast and scalable Bayesian deep learning by weight-perturbation in Adam ME Khan, D Nielsen, V Tangkaratt, W Lin, Y Gal, A Srivastava International Conference on Machine Learning, 2018, 2018 | 75 | 2018 |
| Houdini: Lifelong learning as program synthesis L Valkov, D Chaudhari, A Srivastava, C Sutton, S Chaudhuri Advances in Neural Information Processing Systems, 8687-8698, 2018 | 22 | 2018 |
| Effect of BlueP/MoS2 heterostructure and graphene layer on the performance parameter of SPR sensor: theoretical insight YK Prajapati, A Srivastava Superlattices and Microstructures 129, 152-162, 2019 | 13 | 2019 |
| Performance Analysis of Silicon and Blue Phosphorene/MoS2 Hetero-Structure Based SPR Sensor A Srivastava, YK Prajapati Photonic Sensors 9 (3), 284-292, 2019 | 12 | 2019 |
| Clustering with a reject option: Interactive clustering as bayesian prior elicitation A Srivastava, J Zou, C Sutton KDD 2016 Workshop on Interactive Data Exploration and Analytics (IDEA’16 …, 2016 | 8 | 2016 |
| A theoretical approach to improve the performance of SPR biosensor using MXene and black phosphorus A Srivastava, A Verma, R Das, YK Prajapati Optik 203, 163430, 2020 | 7 | 2020 |
| Neural variational inference for topic models A Srivastava, C Sutton Bayesian deep learning workshop, NIPS 2016, 2016 | 6 | 2016 |
| Species of Scapania from Lilam in Kumaon (western Himalayas), Uttar Pradesh, India SC Srivastava, A Srivastava Geophytology 24, 137-145, 1995 | 6 | 1995 |
| Variational inference in pachinko allocation machines A Srivastava, C Sutton arXiv preprint arXiv:1804.07944, 2018 | 4 | 2018 |
| Synthesis of differentiable functional programs for lifelong learning L Valkov, D Chaudhari, A Srivastava, C Sutton, S Chaudhuri arXiv preprint arXiv:1804.00218, 2018 | 4 | 2018 |
| Ratio matching MMD nets: low dimensional projections for effective deep generative models A Srivastava, K Xu, MU Gutmann, C Sutton stat 1050, 1, 2019 | 3 | 2019 |
| Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference CL Hurwitz, K Xu, A Srivastava, AP Buccino, M Hennig NeurIPS, 2019, 2019 | 3 | 2019 |
| Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability R Hirt, A Srivastava, C Berg, N Kühl arXiv preprint arXiv:2003.13070, 2020 | 2 | 2020 |
| Bregmn: scaled-bregman generative modeling networks A Srivastava, K Greenewald, F Mirzazadeh arXiv preprint arXiv:1906.00313, 2019 | 2 | 2019 |
| Variational Russian Roulette for Deep Bayesian Nonparametrics K Xu, A Srivastava, C Sutton International Conference on Machine Learning, 6963-6972, 2019 | 1 | 2019 |
| Effect of sulfosalt and polymers on performance parameter of SPR biosensor A Srivastava, YK Prajapati Optical and Quantum Electronics 52 (10), 1-14, 2020 | | 2020 |
| not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget S Han, A Srivastava, C Hurwitz, P Sattigeri, DD Cox arXiv preprint arXiv:2009.04433, 2020 | | 2020 |
| Effect of Perovskite material on performance of surface plasmon resonance biosensor A Srivastava, R Das, YK Prajapati IET Optoelectronics, 2020 | | 2020 |
Charles SuttonGoogle, University of EdinburghVerified email at inf.ed.ac.uk
