Akash Srivastava
Akash Srivastava
MIT, IBM Research, University of Edinburgh
Verified email at ibm.com - Homepage
TitleCited byYear
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
Autoencoding Variational Inference for Topic Models
A Srivastava, C Sutton
International Conference on Learning Representations (ICLR), 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
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
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
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
Variational inference in pachinko allocation machines
A Srivastava, C Sutton
arXiv preprint arXiv:1804.07944, 2018
Ratio matching MMD nets: low dimensional projections for effective deep generative models
A Srivastava, K Xu, MU Gutmann, C Sutton
arXiv preprint arXiv:1806.00101, 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
Performance Analysis of Silicon and Blue Phosphorene/MoS2 Hetero-Structure Based SPR Sensor
A Srivastava, YK Prajapati
Photonic Sensors 9 (3), 284-292, 2019
Variational Russian Roulette for Deep Bayesian Nonparametrics
K Xu, A Srivastava, C Sutton
International Conference on Machine Learning, 6963-6972, 2019
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
Deep generative modelling for amortised variational inference
A Srivastava
The University of Edinburgh, 2019
SimVAE: Simulator-Assisted Training for Interpretable Generative Models
A Srivastava, J Rosenberg, D Gutfreund, DD Cox
BreGMN: scaled-Bregman Generative Modeling Networks
A Srivastava, K Greenewald, F Mirzazadeh
arXiv preprint arXiv:1906.00313, 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
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
M Emtiyaz Khan, D Nielsen, V Tangkaratt, W Lin, Y Gal, A Srivastava
arXiv preprint arXiv:1806.04854, 2018
Burst Detection Modulated Document Clustering: A Partially Feature-Pivoted Approach To First Story Detection
A Srivastava
Informatics Forum, University of Edinburgh, 2014
WiMAX–Filters at Different Frequency Spectrums
A Srivastava, S Srivastava
Amortized Inference for Latent Feature Models Using Variational Russian Roulette
K Xu, A Srivastava, C Sutton
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