Lars Maaløe
Artificial Intelligence · 2015–2022
A researcher in the Science corpus: 8 papers; trusts 499 researchers and is trusted by 235 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 365 citations into it; #2,372 of 35,345 by trust here, #3,440 by citations
- Signal Processing — 179 citations into it
- Computer Vision and Pattern Recognition — 176 citations into it; #2,565 of 21,575 by trust here, #2,637 by citations
- Speech & Audio — 155 citations into it
- Representation & Self-Supervised Learning — 135 citations into it
- Generative Models — 107 citations into it; #305 of 1,956 by trust here, #361 by citations
- Probabilistic & Bayesian Methods — 75 citations into it; #472 of 1,525 by trust here, #373 by citations
- Natural Language Processing — 61 citations into it
Whom Lars Maaløe trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Danilo Jimenez Rezende
- Yoshua Bengio
- Geoffrey E. Hinton
- Ruslan Salakhutdinov
- Yann LeCun
- Max Welling
- Alex Krizhevsky
- Jimmy Ba
- Shakir Mohamed
Who trusts Lars Maaløe overall
- Arash Vahdat
- Max Welling
- Diederik P. Kingma
- Qizhe Xie
- Rewon Child
- Rajesh Ranganath
- J. Tomczak
- Takeru Miyato
- Yoshua Bengio
- Zihang Dai
Papers from Lars Maaløe's perspective
Ordered by how strongly the researchers Lars Maaløe 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)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Auxiliary Deep Generative Models (2016) — 467 citations
- Ladder Variational Autoencoders (2016) — 1,005 citations
- BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling (2019) — 228 citations
- Semi-Supervised Generation with Cluster-aware Generative Models (2017) — 29 citations
- A Brief Overview of Unsupervised Neural Speech Representation Learning (2022) — 13 citations
- Self-Supervised Speech Representation Learning: A Review (2022) — 515 citations
- How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks (2016) — 142 citations
- Improving Semi-Supervised Learning with Auxiliary Deep Generative Models (2015) — 28 citations