Sachithra Hemachandra

Artificial Intelligence · 2013–2015

A researcher in the Science corpus: 4 papers; trusts 24 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it
• Computer Vision and Pattern Recognition — 26 citations into it; #12,619 of 21,575 by trust here, #8,722 by citations
• Natural Language Processing — 22 citations into it; #3,449 of 5,688 by trust here, #3,235 by citations
• Aerospace Engineering — 16 citations into it; #3,070 of 6,421 by trust here, #2,193 by citations
• Electrical and Electronic Engineering — 6 citations into it
• Reinforcement Learning — 6 citations into it
• Computer Networks and Communications — 5 citations into it

Whom Sachithra Hemachandra trusts overall

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

1. Stefanie Tellex
2. Michael Kaess
3. Matthew R. Walter
4. Thomas Kollar
5. Joris M. Mooij
6. Arnaud Doucet
7. Edwin Olson
8. Benjamin Kuipers
9. Cynthia Matuszek
10. Ananth Ranganathan

Who trusts Sachithra Hemachandra overall

1. Matthew R. Walter
2. Valts Blukis
3. Boyuan Chen
4. Karthik Narasimhan
5. Michael Janner
6. Felix Duvallet
7. Felix Hill
8. Regina Barzilay
9. Stefanie Tellex
10. Zhuo Xu

Papers from Sachithra Hemachandra's perspective

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

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

Their papers

• Learning Semantic Maps from Natural Language Descriptions (2013) — 111 citations
• Learning models for following natural language directions in unknown environments (2015) — 93 citations
• Learning spatial-semantic representations from natural language descriptions and scene classifications (2014) — 65 citations
• A framework for learning semantic maps from grounded natural language descriptions (2014) — 51 citations