Marinka Zitnik
Molecular Biology · 2013–2024
A researcher in the Science corpus: 26 papers; trusts 524 researchers and is trusted by 54 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 669 citations into it; #827 of 17,773 by trust here, #2,641 by citations
- Artificial Intelligence — 586 citations into it; #7,824 of 35,345 by trust here, #3,551 by citations
- Computational Theory and Mathematics — 341 citations into it; #4,751 of 12,182 by trust here, #1,027 by citations
- Graph Learning — 230 citations into it; #283 of 693 by trust here, #141 by citations
- Computer Vision and Pattern Recognition — 183 citations into it; #16,767 of 21,575 by trust here, #20,756 by citations
- Representation & Self-Supervised Learning — 158 citations into it; #1,853 of 3,145 by trust here, #1,266 by citations
- Materials Chemistry — 155 citations into it; #7,322 of 11,513 by trust here, #5,138 by citations
- Statistical and Nonlinear Physics — 148 citations into it
Whom Marinka Zitnik trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Jure Leskovec
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Thomas Kipf
- Max Welling
- Yann LeCun
- Kaiming He
Who trusts Marinka Zitnik overall
- Petar Velickovic
- Jure Leskovec
- Guohao Li
- Weihua Hu
- William L. Hamilton
- Rex Ying
- Yiding Yang
- Jiaxuan You
- Michelle M. Li
- Kexin Huang
Papers from Marinka Zitnik's perspective
Ordered by how strongly the researchers Marinka Zitnik trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Strategies for Pre-training Graph Neural Networks (2019) — 1,844 citations
- Modeling polypharmacy side effects with graph convolutional networks (2018) — 1,392 citations
- Embedding Logical Queries on Knowledge Graphs (2018) — 336 citations
- Learning Structural Node Embeddings via Diffusion Wavelets (2017) — 444 citations
- Prioritizing network communities (2018) — 78 citations
- Predicting multicellular function through multi-layer tissue networks (2017) — 593 citations
- Evolution of resilience in protein interactomes across the tree of life (2018) — 99 citations
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018) — 586 citations
- Data Fusion by Matrix Factorization (2013) — 209 citations
- Network medicine framework for identifying drug-repurposing opportunities for COVID-19 (2020) — 515 citations
- Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021) — 487 citations
- DeepPurpose: a deep learning library for drug–target interaction prediction (2020) — 447 citations
- Scientific discovery in the age of artificial intelligence (2023) — 1,745 citations
- Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency (2022) — 507 citations
- GNNGuard: Defending Graph Neural Networks against Adversarial Attacks (2020) — 397 citations
- Empowering Biomedical Discovery with AI Agents (2024) — 352 citations
- TrustLLM: Trustworthiness in Large Language Models (2024) — 353 citations
- Building a knowledge graph to enable precision medicine (2022) — 561 citations
- Graph Representation Learning in Biomedicine and Healthcare (2021) — 298 citations
- Open Graph Benchmark: Datasets for Machine Learning on Graphs (2020) — 3,715 citations