Jasmine Hsu

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 8 papers; trusts 252 researchers and is trusted by 167 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 250 citations into it
• Computer Vision and Pattern Recognition — 246 citations into it; #2,820 of 21,575 by trust here, #1,890 by citations
• Reinforcement Learning — 141 citations into it
• Control and Systems Engineering — 105 citations into it; #3,724 of 8,257 by trust here, #351 by citations
• Multimodal Learning — 41 citations into it
• Representation & Self-Supervised Learning — 41 citations into it; #1,097 of 3,145 by trust here, #767 by citations
• Cognitive Neuroscience — 20 citations into it
• Model Architectures — 17 citations into it

Whom Jasmine Hsu trusts overall

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

1. Geoffrey E. Hinton
2. Nitish Srivastava
3. Alex Krizhevsky
4. Yoshua Bengio
5. Diederik P. Kingma
6. Christian Szegedy
7. Kaiming He
8. Yann LeCun
9. Ross B. Girshick
10. Ilya Sutskever

Who trusts Jasmine Hsu overall

1. Faraz Torabi
2. Sergey Levine
3. Pieter Abbeel
4. J. Urgen Schmidhuber
5. Abhishek Gupta
6. A. Srinivas
7. Timothée Lesort
8. Luke Metz
9. Bolei Zhou
10. Corey Lynch

Papers from Jasmine Hsu's perspective

Ordered by how strongly the researchers Jasmine Hsu 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. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet: A large-scale hierarchical image database
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Time-Contrastive Networks: Self-Supervised Learning from Video (2017) — 943 citations
• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (2022) — 3,376 citations
• RT-1: Robotics Transformer for Real-World Control at Scale (2022) — 2,545 citations
• Time-Contrastive Networks: Self-Supervised Learning from Multi-view Observation (2017) — 162 citations
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 (2023) — 1,105 citations
• Learning Grasping Interaction with Geometry-aware 3D Representations (2017) — 8 citations
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control (2023) — 3,674 citations
• Time-Contrastive Networks : Self-Supervised Learning from Pixels (2017)