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May 5, 2022 SE (3)-equivariant prediction of molecular wavefunctions and electronic densities and Equivariant message passing for the prediction of tensorial properties and molecular spectra, (Shen Yuan)
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Apr. 28, 2022 E(n) Equivariant Normalizing Flows, (Fanmeng Wang)
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Apr. 21, 2022 Top-N: Equivariant set and graph generation without exchangeability and Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds, (Minjie Cheng)
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Apr. 14, 2022 Continuous wasserstein-2 barycenter estimation without minimax optimization and Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support, (Fengjiao Gong)
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Apr. 7, 2022 Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes and Counterfactual Temporal Point Processes, (Qingmei Wang)
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Mar. 31, 2022 Comparing Three Notions of Discrete Ricci Curvature on Biological Networks, Wireless network capacity versus Ollivier-Ricci curvature under Heat-Diffusion (HD) protocol and Community detection on networks with Ricci flow, (Yue Xiang)
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Mar. 24, 2022 Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent, (Fanmeng Wang)
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Mar. 17, 2022 LieTransformer: Equivariant Self-Attention for Lie Groups and Sinkformers: Transformers with doubly stochastic attention, (Minjie Cheng)
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Mar. 10, 2022 Fast iterative solution of the optimal transport problem on graphs and Quadratically regularized optimal transport on graphs, (Fengjiao Gong)
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Mar. 3, 2022 Fast Estimation of Causal Interactions using Wold Processes and A Variational Inference Approach to Learning Multivariate Wold Processes, (Qingmei Wang)
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Dec. 30, 2021 Large-Margin Contrastive Learning with Distance Polarization Regularizer, (Minjie Cheng)
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Dec. 23, 2021 Equivariant Subgraph Aggregation Networks, (Shen Yuan)
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Dec. 16, 2021 Large-scale optimal transport map estimation using projection pursuit, (Yue Xiang)
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Dec. 9, 2021 A Regularized Wasserstein Framework for Graph Kernels, (Fengjiao Gong)
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Dec. 2, 2021 Multiple Instance Learning with Bag Dissimilarities and Bag similarity network for deep multi-instance learning, (Qingmei Wang)
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Nov. 25, 2021 Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks and Deep Set, (Minjie Cheng)
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Nov. 18, 2021 Decoupled Contrastive Learning and Debiased Contrastive Learning, (Shen Yuan)
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Nov. 11, 2021 Utility/Privacy Trade-off through the lens of Optimal Transport, (Fengjiao Gong)
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Nov. 4, 2021 Adversarial Graph Augmentation to Improve Graph Contrastive Learning and Prototypical Graph Contrastive Learning, (Qingmei Wang)
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Oct. 28, 2021 Generative models for graph-based protein design and Scaffold-based molecular design with a graph generative model, (Minjie Cheng)
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Oct. 21, 2021 Iterative Amortized Inference and Semi-amortized variational autoencoders, (Shen Yuan)
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Oct. 14, 2021 Reparameterizing the Birkhoff Polytope for Permutation Variational Inference and Learing Latent Permutations with Gumbel-Sinkhorn Networks, (Yue Xiang)
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Sep. 30, 2021 Run-Sort-ReRun: Escaping Batch Size Limitations in Sliced Wasserstein Generative Models, (Fengjiao Gong)
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Sep. 23, 2021 Understanding the Spread of COVID-19 Epidemic: A Spatio-Temporal Point Process View, (Qingmei Wang)
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Sep. 17, 2021 Highly accurate protein structure prediction with AlphaFold, and Accurate prediction of protein structures and interactions using a three-track neural network, (Minjie Cheng)
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Sep. 10, 2021 A Particle-Evolving Method for Approximating the Optimal Transport Plan, (Yue Xiang)
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Sep. 3, 2021 Subgraph Augmentation with Application to Graph Mining, (Shen Yuan)
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Aug. 27, 2021 Deep Fourier Kernel for Self-Attentive Point Processes, (Qingmei Wang)
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Aug. 20, 2021 Drug Recommendation toward Safe Polypharmacy, (Minjie Cheng)
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Aug. 13, 2021 Towards domain-agnostic contrastive learning, (Fengjiao Gong)
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Aug. 6, 2021 RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning, (Shen Yuan)
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Jul. 30, 2021 GTA: Graph Truncated Attention for Retrosynthesis, (Yue Xiang)
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Jun. 25, 2021 Learning to make generalizable and diverse predictions for retrosynthesis, (Yue Xiang)
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Jun. 18, 2021 Retrosynthesis Prediction with Conditional Graph Logic Network, (Shen Yuan)
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Jun. 11, 2021 Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space, (Yue Xiang)
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Jun. 4, 2021 Retrosynthesis of multi-component metal−organic frameworks, (Shen Yuan)
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May 28, 2021 Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction, (Yue Xiang)
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May 21, 2021 Multiview Sensing With Unknown Permutations: An Optimal Transport Approach, (Shen Yuan)
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May 14, 2021 Computer-Assisted Retrosynthesis Based on Molecular Similarity, (Yue Xiang)
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May 7, 2021 A Graph to Graphs Framework for Retrosynthesis Prediction, (Shen Yuan)
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Apr. 30, 2021 Graphon Signal Processing, (Yue Xiang)
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Apr. 23, 2021 Optimal transport mapping via input convex neural networks, (Shen Yuan)
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Apr. 2, 2021 STRATEGIC NETWORK FORMATION WITH MANY AGENTS, (Shen Yuan)
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Mar. 26, 2021 The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation, (Yue Xiang)
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Mar. 19, 2021 Improved Complexity Bounds in the Wasserstein Barycenter Problem, (Zejun Xie)
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Mar. 12, 2021 The Multivariate Hawkes Process in High Dimensions: Beyond Mutual Excitation, (Shen Yuan)
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Mar. 5, 2021 Partial Gromov-Wasserstein Learning for Partial Graph Matching, (Yue Xiang)
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Feb. 26, 2021 Existence and consistency of Wasserstein barycenters, (Zejun Xie)
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Feb. 19, 2021 Fast and Flexible Temporal Point Processes with Triangular Maps, (Shen Yuan)
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Feb. 5, 2021 Online Sinkhorn: Optimal Transport distances from sample streams, (Yue Xiang)
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Jan. 29, 2021 Bayesian Inference for Optimal Transport with Stochastic Cost, (Zejun Xie)
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Jan. 22, 2021 Temporal Logic Point Processes, (Shen Yuan)
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Jan. 15, 2021 Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein, (Yue Xiang)