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Akash Srivastava
Akash Srivastava
MIT, IBM Research, University of Edinburgh
Verified email at mit.edu - Homepage
Title
Cited by
Cited by
Year
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
5322017
Autoencoding Variational Inference for Topic Models
A Srivastava, C Sutton
International Conference on Learning Representations (ICLR), 2017
3882017
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
1882018
Houdini: Lifelong learning as program synthesis
L Valkov, D Chaudhari, A Srivastava, C Sutton, S Chaudhuri
Advances in Neural Information Processing Systems 31, 2018
572018
Neural variational inference for topic models
A Srivastava, C Sutton
ArXiv Preprint 1 (1), 1-12, 2016
132016
Variational russian roulette for deep bayesian nonparametrics
K Xu, A Srivastava, C Sutton
International Conference on Machine Learning, 6963-6972, 2019
122019
Generative Ratio Matching Networks
A Srivastava, MU Gutmann, K Xu, C Sutton
International Conference on Learning Representations, 2020
10*2020
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
CL Hurwitz, K Xu, A Srivastava, AP Buccino, M Hennig
NeurIPS, 2019, 2019
92019
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
92016
Equivariant contrastive learning
R Dangovski, L Jing, C Loh, S Han, A Srivastava, B Cheung, P Agrawal, ...
arXiv preprint arXiv:2111.00899, 2021
72021
Targeted neural dynamical modeling
C Hurwitz, A Srivastava, K Xu, J Jude, M Perich, L Miller, M Hennig
Advances in Neural Information Processing Systems 34, 29379-29392, 2021
52021
Veegan: Reducing mode collapse in gans using implicit variational learning. arXiv 2017
A Srivastava, L Valkov, C Russell, MU Gutmann, C Sutton
arXiv preprint arXiv:1705.07761, 0
5
A Bayesian-symbolic approach to reasoning and learning in intuitive physics
K Xu, A Srivastava, D Gutfreund, F Sosa, T Ullman, J Tenenbaum, ...
Advances in Neural Information Processing Systems 34, 2478-2490, 2021
42021
Variational inference in pachinko allocation machines
A Srivastava, C Sutton
arXiv preprint arXiv:1804.07944, 2018
42018
Synthesis of differentiable functional programs for lifelong learning
L Valkov, D Chaudhari, A Srivastava, C Sutton, S Chaudhuri
arXiv preprint arXiv:1804.00218, 2018
42018
not-so-biggan: Generating high-fidelity images on a small compute budget
S Han, A Srivastava, CL Hurwitz, P Sattigeri, DD Cox
32020
Simulation of Virtual Enterprises: A Multi Intelligent Agent Based System
AK Srivastava
International Journal of Simulation Systems, Science and Technology (IJSSST …, 2005
32005
Improving the reconstruction of disentangled representation learners via multi-stage modelling
A Srivastava, Y Bansal, Y Ding, C Hurwitz, K Xu, B Egger, P Sattigeri, ...
arXiv preprint arXiv:2010.13187, 2020
22020
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
22020
BreGMN: scaled-Bregman Generative Modeling Networks
A Srivastava, K Greenewald, F Mirzazadeh
arXiv preprint arXiv:1906.00313, 2019
22019
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Articles 1–20