Marius Kloft

Artificial Intelligence · 2009–2020

A researcher in the Science corpus: 17 papers; trusts 319 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 436 citations into it; #6,230 of 35,345 by trust here, #6,330 by citations
• Computer Vision and Pattern Recognition — 183 citations into it; #16,012 of 21,575 by trust here, #11,950 by citations
• Computer Networks and Communications — 106 citations into it; #2,249 of 9,886 by trust here, #2,044 by citations
• Representation & Self-Supervised Learning — 86 citations into it
• Signal Processing — 86 citations into it
• Safety, Robustness & Interpretability — 75 citations into it; #617 of 2,251 by trust here, #1,616 by citations
• Statistical Learning Theory — 65 citations into it
• Statistics and Probability — 61 citations into it

Whom Marius Kloft trusts overall

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

1. Pavel Laskov
2. Konrad Rieck
3. Bernhard Schölkopf
4. Ke Wang
5. Francis R. Bach
6. David Martinus Johannes Tax
7. Alexandre B. Tsybakov
8. Geoffrey E. Hinton
9. John Shawe-Taylor
10. Olivier Chapelle

Who trusts Marius Kloft overall

1. Battista Biggio
2. Blaine Nelson
3. Fabio Roli
4. Zuxuan Wu
5. Pavel Laskov
6. Giorgio Fumera
7. Nicholas Carlini
8. Yu-Gang Jiang
9. Nedim Srndic
10. Nicolas Papernot

Papers from Marius Kloft's perspective

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

Their papers

• l p -Norm Multiple Kernel Learning (2011) — 356 citations
• lp-Norm Multiple Kernel Learning (2011) — 338 citations
• Security analysis of online centroid anomaly detection (2010) — 97 citations
• A Unifying Review of Deep and Shallow Anomaly Detection (2020) — 1,067 citations
• Toward Supervised Anomaly Detection (2014) — 418 citations
• Predicting MOOC Dropout over Weeks Using Machine Learning Methods (2014) — 323 citations
• SVM2Motif—Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor (2015) — 9 citations
• ML2Motif—Reliable extraction of discriminative sequence motifs from learning machines (2017) — 8 citations
• Deep Semi-Supervised Anomaly Detection (2019) — 704 citations
• Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation (2018) — 40 citations
• Deep One-Class Classification (2018) — 2,692 citations
• Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning (2016) — 33 citations
• Feature Importance Measure for Non-linear Learning Algorithms (2016) — 40 citations
• Insights from Classifying Visual Concepts with Multiple Kernel Learning (2011) — 11 citations
• Online Anomaly Detection under Adversarial Impact (2010) — 159 citations
• A framework for quantitative security analysis of machine learning (2009) — 76 citations
• Efficient and Accurate Lp-Norm Multiple Kernel Learning (2009) — 260 citations