Thomas Seidl

Artificial Intelligence · 2009–2012

A researcher in the Science corpus: 4 papers; trusts 21 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it
• Signal Processing — 20 citations into it
• Statistical and Nonlinear Physics — 6 citations into it

Whom Thomas Seidl trusts overall

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

1. Charu C. Aggarwal
2. Jiawei Han
3. Ian T. Jolliffe
4. Yixin Chen
5. Arthur P. Dempster
6. Rakesh Agrawal
7. Hans-Peter Kriegel
8. Feng Cao
9. Philip S. Yu

Papers from Thomas Seidl's perspective

Ordered by how strongly the researchers Thomas Seidl 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. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Density-Based Projected Clustering of Data Streams (2012) — 55 citations
• The ClusTree: indexing micro-clusters for anytime stream mining (2011) — 283 citations
• MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering (2010) — 304 citations
• Evaluating Clustering in Subspace Projections of High Dimensional Data (2009) — 311 citations