Ashok Cutkosky
Artificial Intelligence · 2015–2022
A researcher in the Science corpus: 5 papers; trusts 192 researchers and is trusted by 13 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 75 citations into it; #10,646 of 35,345 by trust here, #17,412 by citations
- Optimization & Training — 39 citations into it
- Computer Vision and Pattern Recognition — 30 citations into it
- Model Architectures — 29 citations into it
- Computational Mechanics — 23 citations into it; #2,632 of 10,001 by trust here, #6,582 by citations
- Large Language Models — 18 citations into it
- Management Science and Operations Research — 16 citations into it
- Natural Language Processing — 11 citations into it
Whom Ashok Cutkosky trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Alex Krizhevsky
- John C. Duchi
- Kaiming He
- Elad Hazan
- Yurii E. Nesterov
- Geoffrey E. Hinton
- Yann LeCun
- Yoshua Bengio
- Jimmy Ba
Who trusts Ashok Cutkosky overall
- Sai Praneeth Karimireddy
- Tianbao Yang
- Cyrille W. Combettes
- Zeeshan Akhtar
- Martin Jaggi
- Xingyu Xie
- Yossi Arjevani
- Yiming Ying
- Harsh Mehta
- Lie He
Papers from Ashok Cutkosky's perspective
Ordered by how strongly the researchers Ashok Cutkosky 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Long Range Language Modeling via Gated State Spaces (2022) — 377 citations
- Momentum-Based Variance Reduction in Non-Convex SGD (2019) — 553 citations
- Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomes (2015) — 1,602 citations
- Distributed Stochastic Optimization via Adaptive SGD (2018) — 24 citations
- Momentum Improves Normalized SGD (2020) — 196 citations