Vasant G Honavar

Molecular Biology · 1993–2017

A researcher in the Science corpus: 12 papers; trusts 34 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 21 citations into it; #6,558 of 17,773 by trust here, #13,563 by citations
• Artificial Intelligence — 17 citations into it; #14,800 of 35,345 by trust here, #9,092 by citations
• Model Architectures — 10 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it
• Statistical Learning Theory — 6 citations into it
• Statistics and Probability — 6 citations into it
• Causal Inference & Discovery — 5 citations into it
• Optimization & Training — 5 citations into it; #1,742 of 2,601 by trust here, #894 by citations

Whom Vasant G Honavar trusts overall

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

1. Geoffrey E. Hinton
2. Paul J. Werbos
3. John H. Holland
4. Timur Ash
5. Stephen Grossberg
6. Judea Pearl
7. Vladimir Naumovich Vapni
8. Andrew McCallum
9. Elias Bareinboim
10. Michael I. Jordan

Who trusts Vasant G Honavar overall

1. J. Urgen Schmidhuber
2. Fei Ye
3. Fabrizio Ferrè
4. Yoshua Bengio
5. Elias Bareinboim
6. Rajesh Parekh
7. Sergey Levine
8. Anuj Karpatne
9. Adrian G. Bors

Papers from Vasant G Honavar's perspective

Ordered by how strongly the researchers Vasant G Honavar 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Discriminatively trained Markov model for sequence classification (2005) — 83 citations
• Learn++: an incremental learning algorithm for supervised neural networks (2001) — 946 citations
• Predicting linear B-cell epitopes using string kernels (2008) — 732 citations
• The Promise and Potential of Big Data: A Case for Discovery Informatics (2014) — 25 citations
• Predicting RNA-Protein Interactions Using Only Sequence Information (2011) — 505 citations
• Big Data, Data Science, and Civil Rights (2017) — 22 citations
• Mobile Health Technology Evaluation (2013) — 884 citations
• Transportability from Multiple Environments with Limited Experiments (2013) — 42 citations
• Proceedings of the Genetic and Evolutionary Computation Conference (2002) — 78 citations
• Constructive neural-network learning algorithms for pattern classification (2000) — 41 citations
• Constructive Neural Network Learning Algorithms for Multi-Category Pattern Classification (1995)
• Generative learning structures and processes for generalized connectionist networks (1993)