Adji Bousso Dieng
Artificial Intelligence · 2016–2022
A researcher in the Science corpus: 9 papers; trusts 724 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 184 citations into it; #9,320 of 35,345 by trust here, #7,346 by citations
- Computer Vision and Pattern Recognition — 132 citations into it; #20,912 of 21,575 by trust here, #12,172 by citations
- Generative Models — 101 citations into it; #1,910 of 1,956 by trust here, #1,336 by citations
- Probabilistic & Bayesian Methods — 95 citations into it; #1,091 of 1,525 by trust here, #668 by citations
- Natural Language Processing — 52 citations into it; #3,804 of 5,688 by trust here, #3,826 by citations
- Representation & Self-Supervised Learning — 39 citations into it
- Statistics and Probability — 29 citations into it
- Optimization & Training — 25 citations into it
Whom Adji Bousso Dieng trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Radford M. Neal
- Alex Krizhevsky
- Tomas Mikolov
- Kaiming He
- Danilo Jimenez Rezende
- Yann LeCun
- Ilya Sutskever
Who trusts Adji Bousso Dieng overall
- Maarten Grootendorst
- Junxian He
- Dustin Tran
- Qian Li
- Yoon Kim
- Fei Wang
- David M. Blei
- Dan Friedman
- Samarth Sinha
- Federico Bianchi
Papers from Adji Bousso Dieng's perspective
Ordered by how strongly the researchers Adji Bousso Dieng 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency (2016) — 251 citations
- Edward: A library for probabilistic modeling, inference, and criticism (2016) — 306 citations
- Avoiding Latent Variable Collapse With Generative Skip Models (2018) — 187 citations
- Consistency Regularization for Variational Auto-Encoders (2021) — 80 citations
- Augment and Reduce: Stochastic Inference for Large Categorical Distributions (2018) — 22 citations
- The Vendi Score: A Diversity Evaluation Metric for Machine Learning (2022) — 306 citations
- Topic Modeling in Embedding Spaces (2019) — 871 citations
- Prescribed Generative Adversarial Networks (2019) — 61 citations
- Learning with Reflective Likelihoods (2018) — 5 citations