Mike O'Connor
Computer Vision and Pattern Recognition · 2012–2019
A researcher in the Science corpus: 3 papers; trusts 53 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 40 citations into it
- Artificial Intelligence — 30 citations into it
- Computer Networks and Communications — 23 citations into it
- Hardware and Architecture — 23 citations into it
- Efficiency & Compression — 22 citations into it
- Model Architectures — 13 citations into it
- Optimization & Training — 10 citations into it
Whom Mike O'Connor trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yann LeCun
- Kaiming He
- Yoshua Bengio
- Karen Simonyan
- Christian Szegedy
- Ilya Sutskever
- Sepp Hochreiter
- J. Urgen Schmidhuber
Papers from Mike O'Connor's perspective
Ordered by how strongly the researchers Mike O'Connor 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
- ImageNet: A large-scale hierarchical image database
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Going deeper with convolutions
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks (2017) — 193 citations
- Buddy Compression: Enabling Larger Memory for Deep Learning and HPC Workloads on GPUs (2019) — 65 citations
- Cache-Conscious Wavefront Scheduling (2012) — 422 citations