Alan Hanjalic
Information Systems · 2002–2017
A researcher in the Science corpus: 8 papers; trusts 187 researchers and is trusted by 50 researchers here, as inferred from the citation graph.
Subfields
- Information Systems — 145 citations into it; #1,307 of 8,771 by trust here, #1,265 by citations
- Computer Vision and Pattern Recognition — 86 citations into it; #4,927 of 21,575 by trust here, #8,296 by citations
- Artificial Intelligence — 81 citations into it; #19,287 of 35,345 by trust here, #15,866 by citations
- Management Science and Operations Research — 45 citations into it; #4,761 of 11,946 by trust here, #2,728 by citations
- Recommender Systems — 41 citations into it
- Signal Processing — 28 citations into it; #1,557 of 7,572 by trust here, #5,570 by citations
- Marketing — 25 citations into it
- Statistical and Nonlinear Physics — 24 citations into it
Whom Alan Hanjalic trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Thomas Hofmann
- Yehuda Koren
- Ruslan Salakhutdinov
- Steffen Rendle
- Geoffrey E. Hinton
- Margaret M. Bradley
- Jonathan L. Herlocker
- Diederik P. Kingma
- Yoshua Bengio
- Alex Krizhevsky
Who trusts Alan Hanjalic overall
- Jian Zhou
- Costas I. Cotsaces
- Arthur G. Money
- Weiming Hu
- Jinhui Yuan
- Liping Shen
- Xiao-Ping Zhang
- Mojtaba Khomami Abadi
- N. Nikolaidis
- Sicheng Zhao
Papers from Alan Hanjalic's perspective
Ordered by how strongly the researchers Alan Hanjalic trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- 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)
- Learning Multiple Layers of Features from Tiny Images
- Going deeper with convolutions
Their papers
- CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering (2012) — 339 citations
- Affective video content representation and modeling (2005) — 629 citations
- Adversarial Cross-Modal Retrieval (2017) — 756 citations
- TFMAP: optimizing MAP for top-n context-aware recommendation (2012) — 231 citations
- Collaborative Filtering beyond the User-Item Matrix (2014) — 858 citations
- Mining contextual movie similarity with matrix factorization for context-aware recommendation (2013) — 111 citations
- List-wise learning to rank with matrix factorization for collaborative filtering (2010) — 266 citations
- Shot-boundary detection: unraveled and resolved? (2002) — 552 citations