Matthew V. Mahoney
Computer Networks and Communications · 1999–2005
A researcher in the Science corpus: 6 papers; trusts 27 researchers and is trusted by 72 researchers here, as inferred from the citation graph.
Subfields
- Computer Networks and Communications — 16 citations into it; #341 of 9,886 by trust here, #1,539 by citations
- Artificial Intelligence — 15 citations into it; #2,885 of 35,345 by trust here, #9,275 by citations
- Signal Processing — 11 citations into it; #912 of 7,572 by trust here, #2,221 by citations
- Information Systems — 6 citations into it
Whom Matthew V. Mahoney trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Urgen Schmidhuber
- Stephanie Forrest
- Richard P. Lippmann
- R. Sekar
- M. Burrows
- Daniel Barbará
- N. Sloane
- Marcus Hutter
- William W. Cohen
- Stefan Heil
Who trusts Matthew V. Mahoney overall
- Byron Knoll
- Ke Wang
- Konrad Rieck
- M. Aydin
- Ilya Sutskever
- Blaine Nelson
- José Hernandez-Orallo
- Roberto Perdisci
- Nando de Freitas
- Salvatore J. Stolfo
Papers from Matthew V. Mahoney's perspective
Ordered by how strongly the researchers Matthew V. Mahoney trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- 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)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection (2003) — 635 citations
- Modeling multiple time series for anomaly detection (2005) — 135 citations
- Adaptive weighing of context models for lossless data compression (2005) — 169 citations
- Network traffic anomaly detection based on packet bytes (2003) — 309 citations
- Learning nonstationary models of normal network traffic for detecting novel attacks (2002) — 421 citations
- Text Compression as a Test for Artificial Intelligence (1999)