Ashish Agarwal

Artificial Intelligence · with Barrett · 2010–2019

A researcher in the Science corpus: 6 papers; trusts 76 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it
• Computer Vision and Pattern Recognition — 45 citations into it
• Natural Language Processing — 25 citations into it
• Model Architectures — 17 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Hardware and Architecture — 14 citations into it
• Reinforcement Learning — 14 citations into it
• Signal Processing — 13 citations into it

Whom Ashish Agarwal trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Ilya Sutskever
6. Dimitri P. Bertsekas
7. Yann LeCun
8. J. Urgen Schmidhuber
9. Richard S. Sutton
10. Oriol Vinyals

Who trusts Ashish Agarwal overall

1. Tara N. Sainath
2. Bo Li
3. Maizar Raissi
4. Liang-Chieh Chen
5. Sergey Levine
6. Yoshua Bengio
7. Rohit Prabhavalkar
8. Andrew Zisserman
9. Ruslan Salakhutdinov
10. Quoc V. Le

Papers from Ashish Agarwal's perspective

Ordered by how strongly the researchers Ashish Agarwal trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning (2019) — 92 citations
• Spectral Inference Networks: Unifying Spectral Methods With Deep Learning (2018) — 10 citations
• Spectral Inference Networks: Unifying Deep and Spectral Learning (2018) — 50 citations
• Hallucinations in Neural Machine Translation (2018) — 183 citations
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (2016) — 11,798 citations
• Overlapping experiment infrastructure: more, better, faster experimentation (2010) — 336 citations