Matko Bosnjak
Artificial Intelligence · 2011–2019
A researcher in the Science corpus: 8 papers; trusts 99 researchers and is trusted by 13 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 148 citations into it; #14,267 of 35,345 by trust here, #9,641 by citations
- Natural Language Processing — 68 citations into it; #2,886 of 5,688 by trust here, #2,263 by citations
- Computer Vision and Pattern Recognition — 57 citations into it; #10,720 of 21,575 by trust here, #6,031 by citations
- Molecular Biology — 45 citations into it; #14,130 of 17,773 by trust here, #5,655 by citations
- Representation & Self-Supervised Learning — 35 citations into it
- Reinforcement Learning — 32 citations into it
- Model Architectures — 27 citations into it; #1,244 of 2,278 by trust here, #1,246 by citations
- Optimization & Training — 15 citations into it
Whom Matko Bosnjak trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Urgen Schmidhuber
- Diederik P. Kingma
- Geoffrey E. Hinton
- Ilya Sutskever
- Alex Graves
- Yoshua Bengio
- Alex Krizhevsky
- Yann LeCun
- Kaiming He
- Sepp Hochreiter
Who trusts Matko Bosnjak overall
- Jorge Armando Mendez Mendez
- Tim Rocktaschel
- Amit Zohar
- Brenden K. Petersen
- Alexander L. Gaunt
- Wang-Zhou Dai
- Jacob Devlin
- Robin Manhaeve
- Eric Eaton
- Sebastian Riedel
Papers from Matko Bosnjak's perspective
Ordered by how strongly the researchers Matko Bosnjak 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Programming with a Differentiable Forth Interpreter (2016) — 157 citations
- COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration (2019) — 133 citations
- SCAN: Learning Hierarchical Compositional Visual Concepts (2017) — 134 citations
- emoji2vec: Learning Emoji Representations from their Description (2016) — 305 citations
- A large-scale evaluation of computational protein function prediction (2013) — 1,002 citations
- Towards Neural Theorem Proving at Scale (2018) — 44 citations
- REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms (2011) — 5,771 citations
- Conference on Empirical Methods in Natural Language Processing Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media Socialnlp@emnlp2016 Chairs' Welcome Identifying and Categorizing Disaster-related Tweets Why Do They Leave: Modeling Participation in Online