Suivre
Akash Srivastava
Akash Srivastava
MIT, IBM Research, University of Edinburgh
Adresse e-mail validée de mit.edu - Page d'accueil
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Citée par
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Année
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
7892017
Autoencoding Variational Inference for Topic Models
A Srivastava, C Sutton
International Conference on Learning Representations (ICLR), 2017
6412017
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
2922018
Equivariant contrastive learning
R Dangovski, L Jing, C Loh, S Han, A Srivastava, B Cheung, P Agrawal, ...
arXiv preprint arXiv:2111.00899, 2021
1122021
Houdini: Lifelong learning as program synthesis
L Valkov, D Chaudhari, A Srivastava, C Sutton, S Chaudhuri
Advances in neural information processing systems 31, 2018
912018
Identifiability guarantees for causal disentanglement from soft interventions
J Zhang, K Greenewald, C Squires, A Srivastava, K Shanmugam, C Uhler
Advances in Neural Information Processing Systems 36, 2024
322024
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
232021
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
222021
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 1, 2023
212023
Neural variational inference for topic models
A Srivastava, C Sutton
ArXiv Preprint 1 (1), 1-12, 2016
192016
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
CL Hurwitz, K Xu, A Srivastava, AP Buccino, M Hennig
NeurIPS, 2019, 2019
182019
Variational russian roulette for deep bayesian nonparametrics
K Xu, A Srivastava, C Sutton
International Conference on Machine Learning, 6963-6972, 2019
182019
Beyond statistical similarity: Rethinking metrics for deep generative models in engineering design
L Regenwetter, A Srivastava, D Gutfreund, F Ahmed
Computer-Aided Design 165, 103609, 2023
172023
Compositional foundation models for hierarchical planning
A Ajay, S Han, Y Du, S Li, A Gupta, T Jaakkola, J Tenenbaum, L Kaelbling, ...
Advances in Neural Information Processing Systems 36, 2024
162024
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
132022
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
132016
Generative Ratio Matching Networks
A Srivastava, MU Gutmann, K Xu, C Sutton
International Conference on Learning Representations, 2020
12*2020
Aligning optimization trajectories with diffusion models for constrained design generation
G Giannone, A Srivastava, O Winther, F Ahmed
Advances in Neural Information Processing Systems 36, 2024
102024
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
102023
Learning from invalid data: On constraint satisfaction in generative models
G Giannone, L Regenwetter, A Srivastava, D Gutfreund, F Ahmed
arXiv preprint arXiv:2306.15166, 2023
82023
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