Vijayaraghavan Murali
Artificial Intelligence · 2017–2020
A researcher in the Science corpus: 6 papers; trusts 90 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 78 citations into it; #27,469 of 35,345 by trust here, #19,018 by citations
- Information Systems — 73 citations into it; #1,934 of 8,771 by trust here, #4,682 by citations
- Natural Language Processing — 59 citations into it
- Software — 57 citations into it; #262 of 1,227 by trust here, #572 by citations
- Code Generation & Program Synthesis — 31 citations into it; #84 of 220 by trust here, #170 by citations
- Computer Vision and Pattern Recognition — 21 citations into it
- Signal Processing — 18 citations into it; #5,538 of 7,572 by trust here, #7,112 by citations
- Program Analysis & Verification — 17 citations into it
Whom Vijayaraghavan Murali trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Veselin Raychev
- Diederik P. Kingma
- Geoffrey E. Hinton
- Tomas Mikolov
- Radford M. Neal
- Martin T. Vechev
- Yoshua Bengio
- Andrew Y. Ng
- Huaiyu Zhu
- Alex Graves
Who trusts Vijayaraghavan Murali overall
- Uri Alon
- Swarat Chaudhuri
- Michael Pradel
- Xinyun Chen
- Miltiadis Allamanis
- Matthew Amodio
- Koushik Sen
- Diane Bouchacourt
- Maxwell Nye
- Omer Levy
Papers from Vijayaraghavan Murali's perspective
Ordered by how strongly the researchers Vijayaraghavan Murali 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Programmatically Interpretable Reinforcement Learning (2018) — 417 citations
- Scaffle: bug localization on millions of files (2020) — 40 citations
- Bayesian specification learning for finding API usage errors (2017) — 47 citations
- Finding Likely Errors with Bayesian Specifications (2017) — 5 citations
- Neural Sketch Learning for Conditional Program Generation (2017) — 141 citations
- Bayesian Sketch Learning for Program Synthesis (2017) — 38 citations