Jun Wang

Artificial Intelligence · with Chen · 2020

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 17 citations into it
• Causal Inference & Discovery — 8 citations into it

Whom Jun Wang trusts

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

1. Bernhard Schölkopf (in Causal Inference & Discovery)
2. Yoshua Bengio (in Artificial Intelligence)
3. Aapo Hyvärinen (in Artificial Intelligence)
4. Diederik P. Kingma (in Artificial Intelligence)

Papers from Jun Wang's perspective

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

1. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
2. Disentangling by Factorising
3. Isolating Sources of Disentanglement in Variational Autoencoders
4. Nonlinear causal discovery with additive noise models
5. A Linear Non-Gaussian Acyclic Model for Causal Discovery
6. On the Identifiability of the Post-Nonlinear Causal Model
7. Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
8. Learning Bayesian networks: The combination of knowledge and statistical data
9. In Advances in Neural Information Processing Systems
10. DAGs with NO TEARS: Continuous Optimization for Structure Learning

Their papers

• CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models (2020) — 407 citations