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Artificial intelligence for graphs has achieved remarkable success in modeling complex systems, ranging from dynamic networks in biology to interacting particle systems in physics.
Graph Neural Networks with Generated Parameters for Relation Extraction
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Graph neural networks for particle reconstruction in high energy physics detectors
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Every document owns its structure: Inductive text classification via graph neural networks
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Hierarchical inter-message passing for learning on molecular graphs
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The graph neural network model
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Multimodal deep learning
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Slic superpixels compared to state-of-the-art superpixel methods
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Message passing networks for molecules with tetrahedral chirality
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Multimodal deep autoencoder for human pose recovery
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E. & Darrell, T · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. & Zisserman, A · 2015
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Variational graph auto-encoders
Kipf, T. N. & Welling, M · 2016
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Training and evaluating multimodal word embeddings with large-scale web annotated images
Mao, J., Xu, J., Jing, Y. & Yuille, A · 2016
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Deeply-recursive convolutional network for image super-resolution
Kim, J., Lee, J. K. & Lee, K. M · 2016
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Faster R-CNN: Towards Real-Time object detection with region proposal networks
Ren, S., He, K., Girshick, R. & Sun, J · 2016
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Order matters: Sequence to sequence for sets
Vinyals, O., Bengio, S. & Kudlur, M · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X. & Vandergheynst, P · 2016
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Moleculenet: a benchmark for molecular machine learning
Wu, Z. et al · 2017
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E · 2017
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Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N. & Welling, M · 2017
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Multimodal machine learning: A survey and taxonomy
Baltrušaitis, T., Ahuja, C. & Morency, L.-P · 2017
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Deep multimodal learning: A survey on recent advances and trends
Ramachandram, D. & Taylor, G. W · 2017
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z. & Leskovec, J · 2017
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Residual gated graph convnets
Bresson, X. & Laurent, T · 2017
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Attention is all you need
Vaswani, A. et al · 2017
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Neural message passing for jet physics
Henrion, I. et al · 2017
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Visible machine learning for biomedicine
Yu, M. K. et al · 2018
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Graph networks as learnable physics engines for inference and control
Sanchez-Gonzalez, A. et al · 2018
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Representation learning on graphs with jumping knowledge networks
Xu, K. et al · 2018
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Learning human-object interactions by graph parsing neural networks
Qi, S., Wang, W., Jia, B., Shen, J. & Zhu, S.-C · 2018
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Graph attention networks
Veličković, P. et al · 2018
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Relational inductive biases, deep learning, and graph networks
Battaglia, P. W. et al · 2018
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Neural message passing with edge updates for predicting properties of molecules and materials
Jørgensen, P. B., Jacobsen, K. W. & Schmidt, M. N · 2018
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Using deep learning to model the hierarchical structure and function of a cell
Ma, J. et al · 2018
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Link prediction based on graph neural networks
Zhang, M. & Chen, Y · 2018
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Graph attention networks
Veličković, P. et al · 2018
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Graphite: Iterative generative modeling of graphs
Grover, A., Zweig, A. & Ermon, S · 2019
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Mvae: Multimodal variational autoencoder for fake news detection
Khattar, D., Goud, J. S., Gupta, M. & Varma, V · 2019
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Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
Zitnik, M. et al · 2019
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Spatio-temporal dynamics and semantic attribute enriched visual encoding for video captioning
Aafaq, N., Akhtar, N., Liu, W., Gilani, S. Z. & Mian, A · 2019
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J. & Jegelka, S · 2019
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Graph-based global reasoning networks
Chen, Y. et al · 2019
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Graph-fcn for image semantic segmentation
Lu, Y., Chen, Y., Zhao, D. & Chen, J · 2019
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Multiscale dynamic graph convolutional network for hyperspectral image classification
Wan, S. et al · 2019
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Image classification with hierarchical multigraph networks
Knyazev, B., Lin, X., Amer, M. R. & Taylor, G. W · 2019
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Scene text visual question answering
Biten, A. F. et al · 2019
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Towards vqa models that can read
Singh, A. et al · 2019
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Relation parsing neural network for human-object interaction detection
Zhou, P. & Chi, M · 2019
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Principal neighbourhood aggregation for graph nets
Corso, G., Cavalleri, L., Beaini, D., Liò, P. & Veličković, P · 2020
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Smil: Multimodal learning with severely missing modality
Ma, M. et al · 2021
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Highly accurate protein structure prediction with alphafold
Jumper, J. et al · 2021
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Multi-scale representation learning on proteins
Somnath, V. R., Bunne, C. & Krause, A · 2021
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Applications of deep learning in molecule generation and molecular property prediction
Walters, W. P. & Barzilay, R · 2021
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Multimodal Graph Meta Contrastive Learning , 3657–3661 (Association for Computing Machinery, New York, NY, USA, 2021)
Zhao, F. & Wang, D · 2021
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Huang, L., Ma, D., Li, S., Zhang, X. & Wang, H · 2019
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Attention guided graph convolutional networks for relation extraction
Guo, Z., Zhang, Y. & Lu, W · 2019
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Aspect-based sentiment classification with aspect-specific graph convolutional networks
Zhang, C., Li, Q. & Song, D · 2019
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Learning representations of irregular particle-detector geometry with distance-weighted graph networks
Qasim, S. R., Kieseler, J., Iiyama, Y. & Pierini, M · 2019
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A graph-convolutional neural network model for the prediction of chemical reactivity
Coley, C. W. et al · 2019
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Graph convolutional neural networks for predicting drug-target interactions
Torng, W. & Altman, R. B · 2019
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Multi-modal graph learning for disease prediction
Zheng, S. et al · 2021
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Learning intuitive physics with multimodal generative models
Rezaei-Shoshtari, S., Hogan, F. R., Jenkin, M., Meger, D. & Dudek, G · 2021
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Bronstein, M. M., Bruna, J., Cohen, T. & Veličković, P · 2021
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Fast interactive video object segmentation with graph neural networks
Varga, V. & Lorincz, A · 2021
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Self-constructing graph neural networks to model long-range pixel dependencies for semantic segmentation of remote sensing images
Liu, Q., Kampffmeyer, M., Jenssen, R. & Salberg, A.-B · 2021
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Graph attention neural network for image restoration
Mou, C. & Zhang, J · 2021
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Spatially conditioned graphs for detecting human-object interactions
Zhang, F. Z., Campbell, D. & Gould, S · 2021
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Graph neural networks for natural language processing: A survey
Wu, L. et al · 2021
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Heterogeneous Graph Neural Networks for Multi-label Text Classification
Li, I., Li, T., Li, Y., Dong, R. & Suzumura, T · 2021
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Mention-centered graph neural network for document-level relation extraction
Pan, J., Peng, M. & Zhang, Y · 2021
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Graph neural networks in particle physics
Shlomi, J., Battaglia, P. & Vlimant, J.-R · 2021
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Physics based gnns for locating faults in power grids
Li, W. & Deka, D · 2021
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A graph placement methodology for fast chip design
Mirhoseini, A. et al · 2021
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Directional message passing on molecular graphs via synthetic coordinates
Gasteiger, J., Yeshwanth, C. & Günnemann, S · 2021
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Fast quantum property prediction via deeper 2d and 3d graph networks
Liu, M. et al · 2021
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Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors
Guan, Y. et al · 2021
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Graph networks for molecular design
Mercado, R. et al · 2021
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Fast end-to-end learning on protein surfaces
Sverrisson, F., Feydy, J., Correia, B. E. & Bronstein, M. M · 2021
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Learning over Families of Sets - Hypergraph Representation Learning for Higher Order Tasks , 756–764 (SIAM Activity Group on Data Science, 2021)
Srinivasan, B., Zheng, D. & Karypis, G · 2021
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Edge representation learning with hypergraphs
Jo, J. et al · 2021
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Distilling holistic knowledge with graph neural networks
Zhou, S. et al · 2021
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Collaborative teacher-student learning via multiple knowledge transfer
Sun, L., Gou, J., Yu, B., Du, L. & Tao, D · 2021
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Deep neural networks and tabular data: A survey
Borisov, V. et al · 2021
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Could graph neural networks learn better molecular representation for drug discovery? a comparison study of descriptor-based and graph-based models
Jiang, D. et al · 2021
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Directional graph networks
Beani, D. et al · 2021
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Weisfeiler and lehman go cellular: CW networks
Bodnar, C. et al · 2021
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Rethinking graph transformers with spectral attention
Kreuzer, D., Beaini, D., Hamilton, W. L., Létourneau, V. & Tossou, P · 2021
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Do transformers really perform badly for graph representation?
Ying, C. et al · 2021
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Graph convolutional networks for hyperspectral image classification
Hong, D. et al · 2021
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A guide to machine learning for biologists
Greener, J. G., Kandathil, S. M., Moffat, L. & Jones, D. T · 2022
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Modality competition: What makes joint training of multi-modal network fail in deep learning? (Provably)
Huang, Y., Lin, J., Zhou, C., Yang, H. & Huang, L · 2022
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Multimodal learning with transformers: A survey (2022)
Xu, P., Zhu, X. & Clifton, D. A · 2022
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A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets
Bayoudh, K., Knani, R., Hamdaoui, F. & Mtibaa, A · 2022
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Mitigating modality collapse in multimodal VAEs via impartial optimization
Javaloy, A., Meghdadi, M. & Valera, I · 2022
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Gmc – geometric multimodal contrastive representation learning
Poklukar, P. et al · 2022
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Graph-guided network for irregularly sampled multivariate time series
Zhang, X., Zeman, M., Tsiligkaridis, T. & Zitnik, M · 2022
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Scalable algorithms for physics-informed neural and graph networks
Shukla, K., Xu, M., Trask, N. & Karniadakis, G. E · 2022
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Combinatorial optimization with physics-inspired graph neural networks
Schuetz, M. J. A., Brubaker, J. K. & Katzgraber, H. G · 2022
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Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability
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Differentiable scaffolding tree for molecule optimization
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Pignet: a physics-informed deep learning model toward generalized drug–target interaction predictions
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Recipe for a general, powerful, scalable graph transformer
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