Akash Srivastava
Akash Srivastava
MIT, IBM Research, University of Edinburgh
Verified email at - Homepage
Cited by
Cited by
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
Equivariant self-supervised learning: Encouraging equivariance in representations
R Dangovski, L Jing, C Loh, S Han, A Srivastava, B Cheung, P Agrawal, ...
International Conference on Learning Representations, 2021
Houdini: Lifelong learning as program synthesis
L Valkov, D Chaudhari, A Srivastava, C Sutton, S Chaudhuri
Advances in neural information processing systems 31, 2018
Variational russian roulette for deep bayesian nonparametrics
K Xu, A Srivastava, C Sutton
International Conference on Machine Learning, 6963-6972, 2019
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
Neural variational inference for topic models
A Srivastava, C Sutton
Bayesian deep learning workshop, NIPS 2016, 2016
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
CL Hurwitz, K Xu, A Srivastava, AP Buccino, M Hennig
NeurIPS, 2019, 2019
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
Generative Ratio Matching Networks
A Srivastava, MU Gutmann, K Xu, C Sutton
International Conference on Learning Representations, 2020
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
Links: A dataset of a hundred million planar linkage mechanisms for data-driven kinematic design
A Heyrani Nobari, A Srivastava, D Gutfreund, F Ahmed
International Design Engineering Technical Conferences and Computers and …, 2022
Variational inference in pachinko allocation machines
A Srivastava, C Sutton
arXiv preprint arXiv:1804.07944, 2018
Beyond statistical similarity: Rethinking metrics for deep generative models in engineering design
L Regenwetter, A Srivastava, D Gutfreund, F Ahmed
arXiv preprint arXiv:2302.02913, 2023
Identifiability guarantees for causal disentanglement from soft interventions
J Zhang, C Squires, K Greenewald, A Srivastava, K Shanmugam, C Uhler
arXiv preprint arXiv:2307.06250, 2023
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression
A Srivastava, S Han, K Xu, B Rhodes, MU Gutmann
arXiv preprint arXiv:2305.00869, 2023
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 2, 2020
Improving Negative-Prompt Inversion via Proximal Guidance
L Han, S Wen, Q Chen, Z Zhang, K Song, M Ren, R Gao, Y Chen, D Liu, ...
arXiv preprint arXiv:2306.05414, 2023
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
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