Ðorðe Miladinovic

Representation & Self-Supervised Learning · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 46 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Representation & Self-Supervised Learning — 57 citations into it; #2,214 of 3,145 by trust here, #1,943 by citations
• Artificial Intelligence — 56 citations into it; #22,490 of 35,345 by trust here, #15,866 by citations
• Computer Vision and Pattern Recognition — 55 citations into it
• Generative Models — 24 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it; #1,156 of 1,525 by trust here, #1,176 by citations
• Causal Inference & Discovery — 9 citations into it; #313 of 364 by trust here, #224 by citations

Whom Ðorðe Miladinovic trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Yann LeCun
5. Jan Peters
6. Max Welling
7. Jimmy Ba
8. Bernhard Schölkopf
9. Alex Krizhevsky
10. Hyunjik Kim

Who trusts Ðorðe Miladinovic overall

1. Francesco Locatello
2. Bernhard Schölkopf
3. Sjoerd van Steenkiste
4. Stefan Bauer
5. Felix Leeb
6. Rui Shu
7. Matthew R. O'Shaughnessy
8. Yashas Annadani

Papers from Ðorðe Miladinovic's perspective

Ordered by how strongly the researchers Ðorðe Miladinovic trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness (2018) — 186 citations
• On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset (2019) — 170 citations
• Interventional Robustness of Deep Latent Variable Models (2018) — 28 citations