Francesco Locatello

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 20 papers; trusts 922 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

Artificial Intelligence — 751 citations into it; #6,332 of 35,345 by trust here, #3,638 by citations
Computer Vision and Pattern Recognition — 566 citations into it; #5,406 of 21,575 by trust here, #3,361 by citations
Representation & Self-Supervised Learning — 488 citations into it; #820 of 3,145 by trust here, #433 by citations
Generative Models — 216 citations into it; #1,681 of 1,956 by trust here, #1,382 by citations
Causal Inference & Discovery — 188 citations into it
Signal Processing — 133 citations into it; #4,897 of 7,572 by trust here, #2,343 by citations
Probabilistic & Bayesian Methods — 117 citations into it
Reinforcement Learning — 114 citations into it

Whom Francesco Locatello trusts overall

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

Who trusts Francesco Locatello overall

Papers from Francesco Locatello's perspective

Ordered by how strongly the researchers Francesco Locatello 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)
Long Short-Term Memory
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)
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
GENERATIVE ADVERSARIAL NETS

Their papers

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (2018) — 1,814 citations
On the Fairness of Disentangled Representations (2019) — 250 citations
Are Disentangled Representations Helpful for Abstract Visual Reasoning? (2019) — 219 citations
Weakly-Supervised Disentanglement Without Compromises (2020) — 365 citations
The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA (2019) — 118 citations
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset (2019) — 170 citations
On the Transfer of Disentangled Representations in Realistic Settings (2020) — 89 citations
SOM-VAE: Interpretable Discrete Representation Learning on Time Series (2019) — 155 citations
Toward Causal Representation Learning (2021) — 1,515 citations
Disentangling Factors of Variation Using Few Labels (2019) — 129 citations
Towards Causal Representation Learning (2021) — 361 citations
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style (2021) — 395 citations
Object-Centric Learning with Slot Attention (2020) — 1,186 citations
Is Independence all you need? On the Generalization of Representations Learned from Correlated Data (2020) — 19 citations
Disentangling Factors of Variations Using Few Labels (2020) — 80 citations
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation (2020) — 80 citations
Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series (2018) — 16 citations
Competitive Training of Mixtures of Independent Deep Generative Models (2018) — 31 citations
Boosting Variational Inference: an Optimization Perspective (2017) — 40 citations
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees (2017) — 29 citations