Robert Feldt
Artificial Intelligence · 1998–2018
A researcher in the Science corpus: 7 papers; trusts 25 researchers and is trusted by 13 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 44 citations into it; #14,023 of 35,345 by trust here, #9,275 by citations
- Information Systems — 44 citations into it; #2,533 of 8,771 by trust here, #1,515 by citations
- Safety, Robustness & Interpretability — 16 citations into it
- Software — 15 citations into it; #1,140 of 1,227 by trust here, #602 by citations
- Computer Science Applications — 12 citations into it
- Computer Vision and Pattern Recognition — 11 citations into it
- Statistics and Probability — 9 citations into it
- Signal Processing — 7 citations into it
Whom Robert Feldt trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ian J. Goodfellow
- Tore Dybå
- Nicholas Carlini
- Sepp Hochreiter
- Geoffrey E. Hinton
- Yoshua Bengio
- Nicolas Papernot
- Christian Szegedy
- Alex Krizhevsky
- Yann LeCun
Who trusts Robert Feldt overall
- Barbara A. Kitchenham
- Claes Wohlin
- Mark Harman
- Claus Pahl
- Sebastian Baltes
- Maher Alharby
- Juanan Pereira
- Earl T. Barr
- Nicolò Paternoster
- Jacopo Soldani
Papers from Robert Feldt's perspective
Ordered by how strongly the researchers Robert Feldt 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)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Going deeper with convolutions
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities (2018) — 317 citations
- Guiding Deep Learning System Testing Using Surprise Adequacy (2018) — 496 citations
- Evaluation and Measurement of Software Process Improvement—A Systematic Literature Review (2012) — 299 citations
- Empirical evidence in global software engineering: a systematic review (2010) — 390 citations
- A systematic review of search-based testing for non-functional system properties (2009) — 439 citations
- Systematic Mapping Studies in Software Engineering (2008) — 3,148 citations
- Generating diverse software versions with genetic programming: and experimental study (1998)