George Deligiannidis
Artificial Intelligence · 2012–2022
A researcher in the Science corpus: 5 papers; trusts 117 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 54 citations into it; #18,607 of 35,345 by trust here, #31,361 by citations
- Computer Vision and Pattern Recognition — 44 citations into it
- Generative Models — 42 citations into it
- Probabilistic & Bayesian Methods — 29 citations into it
- Statistics and Probability — 25 citations into it; #6,221 of 12,932 by trust here, #5,126 by citations
- Statistical and Nonlinear Physics — 19 citations into it
- Signal Processing — 10 citations into it
- Model Architectures — 9 citations into it
Whom George Deligiannidis trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Christophe Andrieu
- Gareth O. Roberts
- MARK A. Beaumont
- Arnaud Doucet
- Diederik P. Kingma
- Pierre Del Moral
- Alex Krizhevsky
- Geoffrey E. Hinton
- Kaiming He
- Radford M. Neal
Who trusts George Deligiannidis overall
- Alain Oliviero Durmus
- Matthew M. Graham
- Arnaud Guillin
- Gael M. Martin
- Pierre Monmarch'e
- Amos J. Storkey
- David T. Frazier
Papers from George Deligiannidis's perspective
Ordered by how strongly the researchers George Deligiannidis 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)
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- GENERATIVE ADVERSARIAL NETS
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- A Continuous Time Framework for Discrete Denoising Models (2022) — 435 citations
- Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator (2012) — 322 citations
- The correlated pseudomarginal method (2015) — 106 citations
- Exponential ergodicity of the bouncy particle sampler (2017) — 52 citations
- Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows (2019) — 146 citations