Alexander Lavin

Computer Vision and Pattern Recognition · 2015–2017

A researcher in the Science corpus: 3 papers; trusts 15 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 31 citations into it
• Artificial Intelligence — 30 citations into it; #11,144 of 35,345 by trust here, #14,718 by citations
• Cognitive Neuroscience — 18 citations into it
• Signal Processing — 13 citations into it
• Computer Networks and Communications — 10 citations into it; #2,262 of 9,886 by trust here, #2,852 by citations
• Probabilistic & Bayesian Methods — 9 citations into it

Whom Alexander Lavin trusts overall

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

1. Nikolay Pavlovich Laptev
2. Eamonn J. Keogh
3. Varun Chandola
4. Jessica Lin
5. Francisco Serdio
6. Saeed Amizadeh
7. Arindam Banerjee
8. Ian Flint
9. Ryan P. Adams
10. Jeff Hawkins

Who trusts Alexander Lavin overall

1. Iuliia Kotseruba
2. Mohsin Munir
3. Kyle Hundman
4. Subutai Ahmad
5. Shoaib Ahmed Siddiqui
6. John K. Tsotsos
7. Valentino Constantinou
8. Laura Erhan
9. Andreas R. Dengel
10. Sebastian Schmidl

Papers from Alexander Lavin's perspective

Ordered by how strongly the researchers Alexander Lavin trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. Anomaly detection: A survey
3. ImageNet: A large-scale hierarchical image database
4. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
5. HOT SAX: efficiently finding the most unusual time series subsequence (Industrial Conference on Data Mining)
6. ImageNet classification with deep convolutional neural networks
7. DRAW: A Recurrent Neural Network For Image Generation
8. Long Short-Term Memory
9. Human-level concept learning through probabilistic program induction
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs (2017) — 270 citations
• Unsupervised real-time anomaly detection for streaming data (2017) — 953 citations
• Evaluating Real-Time Anomaly Detection Algorithms -- The Numenta Anomaly Benchmark (2015) — 507 citations