Rabab Kreidieh Ward
Computer Vision and Pattern Recognition · 2006–2021
A researcher in the Science corpus: 12 papers; trusts 386 researchers and is trusted by 30 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 143 citations into it; #5,645 of 21,575 by trust here, #6,426 by citations
- Artificial Intelligence — 89 citations into it; #11,705 of 35,345 by trust here, #9,170 by citations
- Cognitive Neuroscience — 86 citations into it; #3,737 of 10,852 by trust here, #4,925 by citations
- Media Technology — 76 citations into it; #1,001 of 3,621 by trust here, #757 by citations
- Signal Processing — 60 citations into it
- Computational Mechanics — 49 citations into it
- Cellular and Molecular Neuroscience — 44 citations into it; #901 of 2,998 by trust here, #1,652 by citations
- Electrical and Electronic Engineering — 33 citations into it; #3,812 of 16,539 by trust here, #10,714 by citations
Whom Rabab Kreidieh Ward trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Graves
- Li Deng
- Yann LeCun
- Ilya Sutskever
- J. Urgen Schmidhuber
- Alex Krizhevsky
- Tomas Mikolov
- Sepp Hochreiter
Who trusts Rabab Kreidieh Ward overall
- Hui Li
- Xiaojun Wu
- Jiayi Ma
- Mary Kim
- Li Deng
- Fabien Lotte
- Alexandros Iosifidis
- Amor Ben Tanfous
- Samitha Herath
- Hamid Palangi
Papers from Rabab Kreidieh Ward's perspective
Ordered by how strongly the researchers Rabab Kreidieh Ward 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- 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)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval (2015) — 861 citations
- Learning Input and Recurrent Weight Matrices in Echo State Networks (2013) — 19 citations
- Learning Sparse Representations for Human Action Recognition (2012) — 362 citations
- EMG and EOG artifacts in brain computer interface systems: A survey. (2007) — 670 citations
- Image Fusion With Convolutional Sparse Representation (2016) — 870 citations
- Multi-view 3D Reconstruction with Transformers (2021) — 115 citations
- Deep learning for pixel-level image fusion: Recent advances and future prospects (2018) — 655 citations
- Distributed Compressive Sensing: A Deep Learning Approach (2015) — 118 citations
- Using deep stacking network to improve structured compressed sensing with Multiple Measurement Vectors (2013) — 16 citations
- Fast Image/Video Contrast Enhancement Based on Weighted Thresholded Histogram Equalization (2007) — 446 citations
- A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals (2007) — 948 citations
- 14 FROM LINEAR ADAPTIVE FILTERING TO NONLINEAR INFORMATION PROCESSING (2006) — 34 citations