A. Zhavoronkov
Computational Theory and Mathematics · 2015–2023
A researcher in the Science corpus: 11 papers; trusts 17 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Computational Theory and Mathematics — 88 citations into it; #6,597 of 12,182 by trust here, #596 by citations
- Artificial Intelligence — 50 citations into it
- Computer Vision and Pattern Recognition — 37 citations into it
- Generative Models — 33 citations into it
- Optimization & Training — 20 citations into it
- Representation & Self-Supervised Learning — 7 citations into it
Whom A. Zhavoronkov trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Radford M. Neal
- Yoshua Bengio
- Ilya Sutskever
- Yann LeCun
- Ruslan Salakhutdinov
Who trusts A. Zhavoronkov overall
Papers from A. Zhavoronkov's perspective
Ordered by how strongly the researchers A. Zhavoronkov trusts endorse each paper. Same corpus, a different order for every researcher.
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- ImageNet classification with deep convolutional neural networks
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- GENERATIVE ADVERSARIAL NETS
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
Their papers
- Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models (2018) — 910 citations
- Applications of Deep Learning in Biomedicine. (2016) — 643 citations
- Deep learning enables rapid identification of potent DDR1 kinase inhibitors (2019) — 1,252 citations
- The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology (2016) — 323 citations
- druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico. (2017) — 520 citations
- Chemistry42: An AI-Driven Platform for Molecular Design and Optimization (2023) — 162 citations
- Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes (2016) — 33 citations
- Reinforced Adversarial Neural Computer for de Novo Molecular Design (2018) — 325 citations
- Deep biomarkers of human aging: Application of deep neural networks to biomarker development (2016) — 350 citations
- Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data (2016) — 495 citations
- Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare (2015) — 389 citations