, TensorFlow: Large-scale machine learning on heterogeneous systems, 2015.
SLIC superpixels compared to state-of-the-art superpixel methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.11, pp.2274-2282, 2012. ,
A spectral graph uncertainty principle, IEEE Trans. Information Theory, vol.59, issue.7, pp.4338-4356, 2013. ,
Orientation-boosted voxel nets for 3D object recognition, 2016. ,
Contextually guided semantic labeling and search for three-dimensional point clouds, The International Journal of Robotics Research, vol.32, issue.1, pp.19-34, 2013. ,
3D semantic parsing of large-scale indoor spaces, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ,
Diffusion-convolutional neural networks, Advances in Neural Information Processing Systems (NIPS), 2016. ,
Layer normalization. CoRR, 2016. ,
Designing neural network architectures using reinforcement learning, International Conference on Learning Representations (ICLR), 2017. ,
Emergence of scaling in random networks, Science, vol.286, issue.5439, pp.509-512, 1999. ,
Interaction networks for learning about objects, relations and physics, Advances in Neural Information Processing Systems (NIPS), pp.4502-4510, 2016. ,
Laplacian eigenmaps and spectral techniques for embedding and clustering, Advances in Neural Information Processing Systems (NIPS), pp.585-591, 2001. ,
Scheduled sampling for sequence prediction with recurrent neural networks, Advances in Neural Information Processing Systems (NIPS), pp.1171-1179, 2015. ,
Representation learning: A review and new perspectives, IEEE transactions on pattern analysis and machine intelligence, vol.35, pp.1798-1828, 2013. ,
Deep gaussian embedding of attributed graphs: Unsupervised inductive learning via ranking, 2017. ,
NetGAN: Generating graphs via random walks, 2018. ,
Shortest-path kernels on graphs, IEEE International Conference on Data Mining (ICDM), pp.74-81, 2005. ,
Learning shape correspondence with anisotropic convolutional neural networks, Advances in Neural Information Processing Systems (NIPS), pp.3189-3197, 2016. ,
Unstructured point cloud semantic labeling using deep segmentation networks, Eurographics Workshop on 3D Object Retrieval, vol.2, 2017. ,
Generating sentences from a continuous space, CoNLL, pp.10-21, 2016. ,
Fast approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.11, pp.1222-1239, 2001. ,
Dynamic filter networks, Advances in Neural Information Processing Systems (NIPS), 2016. ,
Generative code modeling with graphs, 2018. ,
Geometric deep learning: Going beyond Euclidean data, IEEE Signal Processing Magazine, vol.34, issue.4, pp.18-42, 2017. ,
, , 2013.
Molgan: An implicit generative model for small molecular graphs, 2018. ,
Fast, exact and multi-scale inference for semantic image segmentation with deep Gaussian CRFs, IEEE European Conference on Computer Vision (ECCV), 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01410872
, Dynamical isometry and a mean field theory of RNNs: Gating enables signal propagation in recurrent neural networks, 2018.
Performance of global descriptors for velodyne-based urban object recognition, IEEE Intelligent Vehicles Symposium Proceedings, pp.667-673, 2014. ,
Learning phrase representations using RNN encoder-decoder for statistical machine translation, Conference on Empirical Methods in Natural Language Processing, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01433235
Finding matches in a haystack: A max-pooling strategy for graph matching in the presence of outliers, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2091-2098, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01053675
Discriminative embeddings of latent variable models for structured data, International Conference on Machine Learning (ICML), 2016. ,
Learning combinatorial optimization algorithms over graphs, Advances in Neural Information Processing Systems (NIPS), 2017. ,
Syntax-directed variational autoencoder for structured data, International Conference on Learning Representations (ICLR), 2018. ,
GPU-accelerated Hungarian algorithms for the linear assignment problem, Parallel Computing, vol.57, pp.52-72, 2016. ,
Unsupervised feature learning for classification of outdoor 3D scans, Australasian Conference on Robotics and Automation, vol.2, 2013. ,
Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity, Journal of medicinal chemistry, vol.34, issue.2, pp.786-797, 1991. ,
Convolutional neural networks on graphs with fast localized spectral filtering, Advances in Neural Information Processing Systems (NIPS), 2016. ,
Dimensionality based scale selection in 3D lidar point clouds. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.97-102, 2011. ,
Weighted graph cuts without eigenvectors a multilevel approach, IEEE transactions, issue.11, p.29, 2007. ,
Density estimation using real NVP, 2016. ,
Distinguishing enzyme structures from non-enzymes without alignments, Journal of molecular biology, vol.330, issue.4, pp.771-783, 2003. ,
Kron reduction of graphs with applications to electrical networks, IEEE Trans. on Circuits and Systems, issue.1, pp.150-163, 2013. ,
Convolutional networks on graphs for learning molecular fingerprints, Advances in Neural Information Processing Systems (NIPS), 2015. ,
Graph based convolutional neural network, British Machine Vision Conference (BMVC), 2016. ,
Exploring spatial context for 3d semantic segmentation of point clouds, IEEE International Conference on Computer Vision (ICCV), 3DRMS Workshop, 2017. ,
On the evolution of random graphs, Publ. Math. Inst. Hung. Acad. Sci, vol.5, issue.1, pp.17-60, 1960. ,
A point set generation network for 3D object reconstruction from a single image, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2463-2471, 2017. ,
Superpixel convolutional networks using bilateral inceptions, IEEE European Conference on Computer Vision (ECCV), 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01801019
Neural message passing for quantum chemistry, International Conference on Machine Learning (ICML), pp.1263-1272, 2017. ,
Automatic chemical design using a data-driven continuous representation of molecules, 2016. ,
Recognition of group activities using dynamic probabilistic networks, IEEE International Conference on Computer Vision (ICCV), pp.742-749, 2003. ,
, , 2014.
Fractional max-pooling, 2014. ,
Sparse 3d convolutional neural networks, British Machine Vision Conference (BMVC), vol.9, pp.150-151, 2015. ,
3d semantic segmentation with submanifold sparse convolutional networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. ,
node2vec: Scalable feature learning for networks, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.855-864, 2016. ,
Weakly supervised segmentation-aided classification of urban scenes from 3D LiDAR point clouds, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01497548
, Semantic3d. net: A new large-scale point cloud classification benchmark, 2017.
Fast semantic segmentation of 3D point clouds with strongly varying density. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, vol.3, issue.3, 2016. ,
, Representation learning on graphs: Methods and applications, 2017.
Inductive representation learning on large graphs, Advances in Neural Information Processing Systems (NIPS), pp.1025-1035, 2017. ,
Convolutional neural networks at constrained time cost, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. ,
Deep residual learning for image recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ,
Approximation capabilities of multilayer feedforward networks, Neural Networks, vol.4, issue.2, pp.251-257, 1991. ,
Efficient 3-d scene analysis from streaming data, IEEE International Conference on Robotics and Automation (ICRA), 2013. ,
Densely connected convolutional networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ,
Point cloud labeling using 3D convolutional neural network, 2016. ,
Recurrent slice networks for 3D segmentation on point clouds, 2018. ,
Batch normalization: Accelerating deep network training by reducing internal covariate shift, International Conference on Machine Learning (ICML), 2015. ,
ZINC: A free tool to discover chemistry for biology, Journal of Chemical Information and Modeling, vol.52, issue.7, pp.1757-1768, 2012. ,
Image-to-image translation with conditional adversarial networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5967-5976, 2017. ,
Spatial transformer networks, Advances in Neural Information Processing Systems (NIPS), pp.2017-2025, 2015. ,
Categorical reparameterization with gumbelsoftmax, 2016. ,
Relative neighborhood graphs and their relatives, Proceedings of the IEEE, vol.80, issue.9, pp.1502-1517, 1992. ,
Junction tree variational autoencoder for molecular graph generation, International Conference on Machine Learning (ICML), pp.2328-2337, 2018. ,
Learning graphical state transitions, International Conference on Learning Representations (ICLR), 2017. ,
Molecular graph convolutions: moving beyond fingerprints, Journal of Computer-Aided Molecular Design, vol.30, issue.8, pp.595-608, 2016. ,
3D scene understanding by voxel-CRF, IEEE International Conference on Computer Vision (ICCV), 2013. ,
Adam: A method for stochastic optimization, International Conference on Learning Representations (ICLR), 2015. ,
Improving variational inference with inverse autoregressive flow, 2016. ,
Auto-encoding variational bayes, 2013. ,
Variational graph auto-encoders, Advances in Neural Information Processing Systems (NIPS), Workshop on Bayesian Deep Learning, 2016. ,
Escape from cells: Deep Kd-networks for the recognition of 3D point cloud models, 2017. ,
N-body networks: a covariant hierarchical neural network architecture for learning atomic potentials, 2018. ,
Covariant compositional networks for learning graphs, 2018. ,
Semantic labeling of 3D point clouds for indoor scenes, Advances in Neural Information Processing Systems (NIPS), pp.244-252, 2011. ,
Distance metric learning using graph convolutional networks: Application to functional brain networks, In Medical Image Computing and Computer-Assisted Intervention, 2017. ,
On information and sufficiency, The Annals of Mathematical Statistics, vol.22, issue.1, pp.79-86, 1951. ,
GANS for sequences of discrete elements with the gumbel-softmax distribution, 2016. ,
Grammar variational autoencoder, International Conference on Machine Learning (ICML), pp.1945-1954, 2017. ,
Cut pursuit: Fast algorithms to learn piecewise constant functions on general weighted graphs, SIAM Journal on Imaging Sciences, vol.10, issue.4, pp.1724-1766, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01306779
A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds, ISPRS Journal of Photogrammetry and Remote Sensing, vol.132, pp.102-118, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01505245
Large-scale point cloud semantic segmentation with superpoint graphs, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01801186
RDKit: Open-source cheminformatics, 2011. ,
Learning arbitrary potentials in CRFs with gradient descent, 2017. ,
Deep projective 3D semantic segmentation, 2017. ,
Deep learning, Nature, vol.521, issue.7553, pp.436-444, 2015. ,
Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, pp.2278-2324, 1998. ,
Photo-realistic single image super-resolution using a generative adversarial network, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.105-114, 2017. ,
Deriving neural architectures from sequence and graph kernels, 2017. ,
, , 2018.
FPNN: field probing neural networks for 3d data, Advances in Neural Information Processing Systems (NIPS), pp.307-315, 2016. ,
Generative moment matching networks, International Conference on Machine Learning (ICML), pp.1718-1727, 2015. ,
Gated graph sequence neural networks, International Conference on Learning Representations (ICLR), 2016. ,
Learning deep generative models of graphs, International Conference on Machine Learning (ICML), 2018. ,
Diffusion convolutional recurrent neural network: Data-driven traffic forecasting, International Conference on Learning Representations, 2018. ,
Multi-objective de novo drug design with conditional graph generative model, J. Cheminformatics, vol.10, issue.1, p.24, 2018. ,
Interpretable structure-evolving LSTM, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2175-2184, 2017. ,
Semantic object parsing with graph LSTM, IEEE European Conference on Computer Vision (ECCV), pp.125-143, 2016. ,
Efficient piecewise training of deep structured models for semantic segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ,
Constrained graph variational autoencoders for molecule design, 2018. ,
Fully convolutional networks for semantic segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3431-3440, 2015. ,
Simplified Markov random fields for efficient semantic labeling of 3d point clouds, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.2690-2697, 2012. ,
Adversarial autoencoders, 2015. ,
3D all the way: Semantic segmentation of urban scenes from start to end in 3D, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. ,
Geodesic convolutional neural networks on Riemannian manifolds, IEEE International Conference on Computer Vision (ICCV), Workshop, pp.37-45, 2015. ,
Shape classification using spectral graph wavelets, Appl. Intell, vol.47, issue.4, pp.1256-1269, 2017. ,
Voxnet: A 3D convolutional neural network for real-time object recognition, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015. ,
Practical graph isomorphism, II, Journal of Symbolic Computation, vol.60, issue.0, pp.94-112, 2014. ,
Efficient estimation of word representations in vector space, 2013. ,
All you need is a good init, International Conference on Learning Representations (ICLR), 2016. ,
Geometric deep learning on graphs and manifolds using mixture model CNNs, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5425-5434, 2017. ,
Motifnet: a motif-based graph convolutional network for directed graphs, 2018. ,
Convolutional neural networks over tree structures for programming language processing, AAAI Conference on Artificial Intelligence, pp.1287-1293, 2016. ,
Contextual classification with functional max-margin Markov networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009. ,
, subgraph2vec: Learning distributed representations of rooted sub-graphs from large graphs, 2016.
Contextual classification of lidar data and building object detection in urban areas, ISPRS Journal of Photogrammetry and Remote Sensing, vol.87, pp.152-165, 2014. ,
Learning convolutional neural networks for graphs, International Conference on Machine Learning (ICML), 2016. ,
Molecular de novo design through deep reinforcement, 2017. ,
Automatic differentiation in PyTorch, Advances in Neural Information Processing Systems (NIPS), 2017. ,
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 1988. ,
Deepwalk: online learning of social representations, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.701-710, 2014. ,
GSPBOX: A toolbox for signal processing on graphs, 2014. ,
PointNet: Deep learning on point sets for 3D classification and segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ,
Volumetric and multi-view CNNs for object classification on 3D data, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ,
PointNet++: Deep hierarchical feature learning on point sets in a metric space, Advances in Neural Information Processing Systems (NIPS), 2017. ,
3D graph neural networks for RGBD semantic segmentation, IEEE International Conference on Computer Vision (ICCV), pp.5209-5218, 2017. ,
, Unsupervised representation learning with deep convolutional generative adversarial networks, 2015.
Quantum chemistry structures and properties of 134 kilo molecules, p.1, 2014. ,
, , 2016.
, Generative adversarial text to image synthesis, International Conference on Machine Learning (ICML), pp.1060-1069
OctNetFusion: Learning depth fusion from data, Proceedings of the International Conference on 3D Vision, 2017. ,
OctNet: Learning deep 3D representations at high resolutions, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ,
3D is here: Point cloud library (pcl), IEEE International Conference on Robotics and Automation (ICRA), pp.1-4, 2011. ,
Designing random graph models using variational autoencoders with applications to chemical design, 2018. ,
On the graph fourier transform for directed graphs, J. Sel. Topics Signal Processing, vol.11, issue.6, pp.796-811, 2017. ,
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks, International Conference on Learning Representations, 2014. ,
The graph neural network model, IEEE Trans. Neural Networks, vol.20, issue.1, pp.61-80, 2009. ,
Unsupervised anomaly detection with generative adversarial networks to guide marker discovery, Information Processing in Medical Imaging (IPMI), pp.146-157, 2017. ,
, Modeling relational data with graph convolutional networks, 2017.
Learning with kernels: support vector machines, regularization, optimization, and beyond, 2002. ,
Quantum-chemical insights from deep tensor neural networks, 2017. ,
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions, Advances in Neural Information Processing Systems (NIPS), pp.992-1002, 2017. ,
Fully connected deep structured networks, 2015. ,
Generating focused molecule libraries for drug discovery with recurrent neural networks, ACS Central Science, vol.4, issue.1, pp.120-131, 2018. ,
Spatial inference machines, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013. ,
Deep learning in medical image analysis, Annual review of biomedical engineering, vol.19, pp.221-248, 2017. ,
Weisfeiler-lehman graph kernels, Journal of Machine Learning Research, vol.12, pp.2539-2561, 2011. ,
A multiscale pyramid transform for graph signals, IEEE Trans. Signal Processing, vol.64, issue.8, pp.2119-2134, 2016. ,
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains, IEEE Signal Processing Magazine, vol.30, issue.3, pp.83-98, 2013. ,
Chebyshev polynomial approximation for distributed signal processing, Distributed Computing in Sensor Systems (DCOSS), pp.1-8, 2011. ,
A deep metric for multimodal registration, Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp.10-18, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01576914
OnionNet: Sharing features in cascaded deep classifiers, British Machine Vision Conference (BMVC), 2016. ,
Dynamic edge-conditioned filters in convolutional neural networks on graphs, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01576919
GraphVAE: Towards generation of small graphs using variational autoencoders, International Conference on Artificial Neural Networks (ICANN), 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01990381
Towards variational generation of small graphs, International Conference on Learning Representations (ICLR), 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01801194
Concerning nonnegative matrices and doubly stochastic matrices, Pacific Journal of Mathematics, vol.21, issue.2, pp.343-348, 1967. ,
Estimation and prediction for stochastic blockmodels for graphs with latent block structure, Journal of Classification, vol.14, issue.1, pp.75-100, 1997. ,
Learning structured output representation using deep conditional generative models, Advances in Neural Information Processing Systems (NIPS), pp.3483-3491, 2015. ,
Graph sparsification by effective resistances, SIAM Journal on Computing, vol.40, issue.6, pp.1913-1926, 2011. ,
End-to-end people detection in crowded scenes, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2325-2333, 2016. ,
Multi-view convolutional neural networks for 3D shape recognition, IEEE International Conference on Computer Vision (ICCV), 2015. ,
Generating text with recurrent neural networks, International Conference on Machine Learning (ICML), pp.1017-1024, 2011. ,
Can recurrent neural networks warp time, International Conference on Learning Representations (ICLR), 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01812064
Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs, IEEE International Conference on Computer Vision (ICCV), 2017. ,
Tangent convolutions for dense prediction in 3D, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. ,
SEGCloud: Semantic segmentation of 3D point clouds, 2017. ,
A note on the evaluation of generative models, 2015. ,
Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds, 2018. ,
Wasserstein autoencoders, International Conference on Learning Representations (ICLR), 2018. ,
Learning superpixels with segmentation-aware affinity loss, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. ,
Pixel recurrent neural networks, International Conference on Machine Learning (ICML), pp.1747-1756, 2016. ,
, Graph Attention Networks. International Conference on Learning Representations (ICLR), 2018.
Generating superpixels with deep representations, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Deep Vision Workshop, 2018. ,
FeaStNet: Feature-steered graph convolutions for 3D shape analysis, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01540389
Order matters: Sequence to sequence for sets, 2015. ,
Comparison of descriptor spaces for chemical compound retrieval and classification, Knowledge and Information Systems, vol.14, issue.3, pp.347-375, 2008. ,
O-CNN: Octree-based convolutional neural networks for 3d shape analysis, ACM Transactions on Graphics, issue.4, p.36, 2017. ,
Non-local neural networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. ,
, Dynamic graph CNN for learning on point clouds, 2018.
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules, Journal of Chemical Information and Computer Sciences, vol.28, issue.1, pp.31-36, 1988. ,
A hybrid semantic point cloud classification-segmentation framework based on geometric features and semantic rules, PFG-Journal of Photogrammetry, Remote Sensing and Geoinformation Science, vol.85, issue.3, pp.183-194, 2017. ,
Contextual classification of point cloud data by exploiting individual 3D neighborhoods. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.4, pp.271-278, 2015. ,
A learning algorithm for continually running fully recurrent neural networks, Neural Computation, vol.1, issue.2, pp.270-280, 1989. ,
Fast semantic segmentation of 3D point clouds using a dense CRF with learned parameters, IEEE International Conference on Robotics and Automation (ICRA), 2015. ,
Learned watershed: End-to-end learning of seeded segmentation, IEEE International Conference on Computer Vision (ICCV), pp.2030-2038, 2017. ,
Moleculenet: a benchmark for molecular machine learning, Chemical science, vol.9, issue.2, pp.513-530, 2018. ,
3D ShapeNets for 2.5D object recognition and next-best-view prediction, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. ,
3D ShapeNets: A deep representation for volumetric shapes, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1912-1920, 2015. ,
Scene graph generation by iterative message passing, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3097-3106, 2017. ,
Deep graph kernels, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015. ,
SyncSpecCNN: Synchronized spectral CNN for 3D shape segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.6584-6592, 2017. ,
Graph convolutional policy network for goal-directed molecular graph generation, 2018. ,
GraphRNN: A deep generative model for graphs, International Conference on Machine Learning (ICML), 2018. ,
SeqGAN: Sequence generative adversarial nets with policy gradient, AAAI Conference on Artificial Intelligence, 2017. ,
Temporal dynamic graph LSTM for action-driven video object detection, IEEE International Conference on Computer Vision (ICCV), pp.1819-1828, 2017. ,
An end-to-end deep learning architecture for graph classification, AAAI Conference on Artificial Intelligence, 2018. ,
Conditional random fields as recurrent neural networks, IEEE International Conference on Computer Vision (ICCV), 2015. ,