Akash Srivastava
Artificial Intelligence · 2017–2023
A researcher in the Science corpus: 8 papers; trusts 298 researchers and is trusted by 26 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 121 citations into it; #7,745 of 35,345 by trust here, #6,229 by citations
- Computer Vision and Pattern Recognition — 74 citations into it; #10,718 of 21,575 by trust here, #7,176 by citations
- Probabilistic & Bayesian Methods — 56 citations into it; #1,162 of 1,525 by trust here, #781 by citations
- Generative Models — 48 citations into it; #994 of 1,956 by trust here, #807 by citations
- Optimization & Training — 32 citations into it; #1,903 of 2,601 by trust here, #1,468 by citations
- Natural Language Processing — 17 citations into it
- Statistics and Probability — 17 citations into it
- Representation & Self-Supervised Learning — 15 citations into it
Whom Akash Srivastava trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- David M. Blei
- Geoffrey E. Hinton
- Ronald J. Williams
- Yoshua Bengio
- Alex Krizhevsky
- Radford M. Neal
- Yann LeCun
- Max Welling
- Danilo Jimenez Rezende
Who trusts Akash Srivastava overall
- Fei Ye
- Adji Bousso Dieng
- Ali Borji
- Sayeri Lala
- Adrian G. Bors
- Federico Bianchi
- Pankaj Gupta
- Weonyoung Joo
- Hao Zhang
- Dan Friedman
Papers from Akash Srivastava's perspective
Ordered by how strongly the researchers Akash Srivastava trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning (2017) — 740 citations
- Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam (2018) — 296 citations
- HOUDINI: Lifelong Learning as Program Synthesis (2018) — 86 citations
- Autoencoding Variational Inference For Topic Models (2017) — 675 citations
- not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget (2020) — 10 citations
- A Probabilistic Framework for Modular Continual Learning (2023) — 5 citations
- Variational Russian Roulette for Deep Bayesian Nonparametrics (2019) — 14 citations
- Proceedings for the 5th International Conference on Learning Representations (2017) — 61 citations