Trishul M. Chilimbi
Artificial Intelligence · 2009–2022
A researcher in the Science corpus: 5 papers; trusts 90 researchers and is trusted by 27 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 73 citations into it
- Computer Vision and Pattern Recognition — 71 citations into it
- Hardware and Architecture — 16 citations into it; #399 of 1,551 by trust here, #954 by citations
- Computer Networks and Communications — 8 citations into it; #3,055 of 9,886 by trust here, #5,961 by citations
- Signal Processing — 5 citations into it
Whom Trishul M. Chilimbi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yann LeCun
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yoshua Bengio
- L. Eon Bottou
- Matthew D. Zeiler
- Ilya Sutskever
- Patrice Y. Simard
- Rob Fergus
- Kaiming He
Who trusts Trishul M. Chilimbi overall
- Jakub Konecný
- Dan Alistarh
- Aaron Harlap
- Tal Ben-Nun
- Suyog Gupta
- Sparsh Mittal
- D. Narayanan
- Hamid Reza Feyzmahdavian
- Jian Zhang
- Henggang Cui
Papers from Trishul M. Chilimbi's perspective
Ordered by how strongly the researchers Trishul M. Chilimbi 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)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Vision-Language Pre-Training with Triple Contrastive Learning (2022) — 392 citations
- SPEED: precise and efficient static estimation of program computational complexity (2009) — 364 citations
- MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud (2022) — 57 citations
- Project Adam: Building an Efficient and Scalable Deep Learning Training System (2014) — 827 citations
- Green: a framework for supporting energy-conscious programming using controlled approximation (2010) — 493 citations