Diviyan Kalainathan

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 5 papers; trusts 60 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it; #24,999 of 35,345 by trust here, #19,439 by citations
• Causal Inference & Discovery — 46 citations into it; #265 of 364 by trust here, #170 by citations
• Statistics and Probability — 29 citations into it
• Molecular Biology — 17 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Signal Processing — 15 citations into it
• Computational Theory and Mathematics — 14 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it

Whom Diviyan Kalainathan trusts overall

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

1. Jan Peters
2. Tom L. Burr
3. David Maxwell Chickering
4. Dominik Janzing
5. Peter Buhlmann
6. Bernhard Schölkopf
7. Ioannis Tsamardinos
8. Diederik P. Kingma
9. Joris M. Mooij
10. Arthur Gretton

Who trusts Diviyan Kalainathan overall

1. Philippe Brouillard
2. Sébastien Lachapelle
3. Matthew James Vowels
4. Xun Zheng
5. Lars Lorch
6. Yue Yu
7. Meike Nauta
8. Ignavier Ng
9. Bernhard Schölkopf
10. Necati Cihan Camgöz

Papers from Diviyan Kalainathan's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. GENERATIVE ADVERSARIAL NETS
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Causal Generative Neural Networks (2017) — 69 citations
• Graph inference datasets. Replication Data for: "Learning Functional Causal Models with Generative Neural Networks" (2017) — 35 citations
• Structural Agnostic Modeling: Adversarial Learning of Causal Graphs (2018) — 121 citations
• Causal Discovery Toolbox: Uncover causal relationships in Python (2019) — 96 citations
• SAM: Structural Agnostic Model, Causal Discovery and Penalized Adversarial Learning (2018) — 47 citations