Matthew Mattina
Artificial Intelligence · 2010–2021
A researcher in the Science corpus: 5 papers; trusts 137 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 86 citations into it; #22,659 of 35,345 by trust here, #17,412 by citations
- Computer Vision and Pattern Recognition — 59 citations into it; #14,030 of 21,575 by trust here, #14,822 by citations
- Efficiency & Compression — 41 citations into it
- Optimization & Training — 21 citations into it
- Model Architectures — 19 citations into it
- Federated & Distributed Learning — 15 citations into it
- Signal Processing — 8 citations into it
- Computational Mechanics — 7 citations into it
Whom Matthew Mattina trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Yann LeCun
- Yoshua Bengio
- Geoffrey E. Hinton
- Kaiming He
- Song Han
- Diederik P. Kingma
- Ilya Sutskever
- L. Eon Bottou
- Karen Simonyan
Who trusts Matthew Mattina overall
Papers from Matthew Mattina's perspective
Ordered by how strongly the researchers Matthew Mattina trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers (2019) — 180 citations
- Learning low-precision neural networks without Straight-Through Estimator(STE) (2019) — 43 citations
- Compressing RNNs for IoT devices by 15-38x using Kronecker Products (2019) — 36 citations
- Federated Learning Based on Dynamic Regularization (2021) — 1,176 citations
- Processor: A 64-Core SoC with Mesh Interconnect (2010) — 507 citations