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Found 58 papers, 24 papers with code
Generalizable Face Landmarking Guided by Conditional Face Warping
Given a pair of real and stylized facial images, the conditional face warper predicts a warping field from the real face to the stylized one, in which the face landmarker predicts the ending points of the warping field and provides us with high-quality pseudo landmarks for the corresponding stylized facial images.
To Search or to Recommend: Predicting Open-App Motivation with Neural Hawkes Process
The model, referred to as Neural Hawkes Process-based Open-App Motivation prediction model (NHP-OAM), employs a hierarchical transformer and a novel intensity function to encode multiple factors, and open-app motivation prediction layer to integrate time and user-specific information for predicting users' open-app motivations.
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications
Geometric graph is a special kind of graph with geometric features, which is vital to model many scientific problems.
Sliceformer: Make Multi-head Attention as Simple as Sorting in Discriminative Tasks
The effectiveness of the Transformer is often attributed to its multi-head attention (MHA) mechanism.
Robust Graph Matching Using An Unbalanced Hierarchical Optimal Transport Framework
Given two graphs, we align their node embeddings within the same modality and across different modalities, respectively.
A Quasi-Wasserstein Loss for Learning Graph Neural Networks
To eliminate such inconsistency, in this study we propose a novel Quasi-Wasserstein (QW) loss with the help of the optimal transport defined on graphs, leading to new learning and prediction paradigms of GNNs.
Decentralized Entropic Optimal Transport for Privacy-preserving Distributed Distribution Comparison
Privacy-preserving distributed distribution comparison measures the distance between the distributions whose data are scattered across different agents in a distributed system and cannot be shared among the agents.
Text2Poster: Laying out Stylized Texts on Retrieved Images
Poster generation is a significant task for a wide range of applications, which is often time-consuming and requires lots of manual editing and artistic experience.
HOTNAS: Hierarchical Optimal Transport for Neural Architecture Search
To address these issues, we propose a hierarchical optimal transport metric called HOTNN for measuring the similarity of different networks.
Regularized Optimal Transport Layers for Generalized Global Pooling Operations
Making the parameters of the ROT problem learnable, we develop a family of regularized optimal transport pooling (ROTP) layers.
Predicting Protein-Ligand Binding Affinity via Joint Global-Local Interaction Modeling
The prediction of protein-ligand binding affinity is of great significance for discovering lead compounds in drug research.
ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery
The last decade has witnessed a prosperous development of computational methods and dataset curation for AI-aided drug discovery (AIDD).
Uni-Mol: A Universal 3D Molecular Representation Learning Framework
Uni-Mol is composed of two models with the same SE(3)-equivariant transformer architecture: a molecular pretraining model trained by 209M molecular conformations; a pocket pretraining model trained by 3M candidate protein pocket data.
Ranked #1 on
Molecular Property Prediction
on MUV
Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction
As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems.
Hilbert Curve Projection Distance for Distribution Comparison
Distribution comparison plays a central role in many machine learning tasks like data classification and generative modeling.
Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification
As a valid metric of metric-measure spaces, Gromov-Wasserstein (GW) distance has shown the potential for matching problems of structured data like point clouds and graphs.
Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction
When applying multi-instance learning (MIL) to make predictions for bags of instances, the prediction accuracy of an instance often depends on not only the instance itself but also its context in the corresponding bag.
Revisiting Global Pooling through the Lens of Optimal Transport
Global pooling is one of the most significant operations in many machine learning models and tasks, whose implementation, however, is often empirical in practice.
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
Many representative graph neural networks, e. g., GPR-GNN and ChebNet, approximate graph convolutions with graph spectral filters.
GPR
Node Classification on Non-Homophilic (Heterophilic) Graphs
Learning Graphon Autoencoders for Generative Graph Modeling
A linear graphon factorization model works as a decoder, leveraging the latent representations to reconstruct the induced graphons (and the corresponding observed graphs).
Hawkes Processes on Graphons
We propose a novel framework for modeling multiple multivariate point processes, each with heterogeneous event types that share an underlying space and obey the same generative mechanism.
Learning Graphons via Structured Gromov-Wasserstein Barycenters
Accordingly, given a set of graphs generated by an underlying graphon, we learn the corresponding step function as the Gromov-Wasserstein barycenter of the given graphs.
A Hypergradient Approach to Robust Regression without Correspondence
Due to the combinatorial nature of the problem, most existing methods are only applicable when the sample size is small, and limited to linear regression models.
Hierarchical Optimal Transport for Robust Multi-View Learning
Traditional multi-view learning methods often rely on two assumptions: ($i$) the samples in different views are well-aligned, and ($ii$) their representations in latent space obey the same distribution.
Learning Autoencoders with Relational Regularization
A new algorithmic framework is proposed for learning autoencoders of data distributions.
Quaternion Product Units for Deep Learning on 3D Rotation Groups
We propose a novel quaternion product unit (QPU) to represent data on 3D rotation groups.
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework.
Gromov-Wasserstein Factorization Models for Graph Clustering
The model achieves a novel and flexible factorization mechanism under GW discrepancy, in which both the observed graphs and the learnable atoms can be unaligned and with different sizes.
Learning to Recommend from Sparse Data via Generative User Feedback
To address this, we propose a learning framework that improves collaborative filtering with a synthetic feedback loop (CF-SFL) to simulate the user feedback.
Zero-Shot Recognition via Optimal Transport
{Specifically, we build a conditional generative model to generate features from seen-class attributes, and establish an optimal transport between the distribution of the generated features and that of the real features.}
Fused Gromov-Wasserstein Alignment for Hawkes Processes
Accordingly, the learned optimal transport reflects the correspondence between the event types of these two Hawkes processes.
Adversarial Self-Paced Learning for Mixture Models of Hawkes Processes
Instead of learning a mixture model directly from a set of event sequences drawn from different Hawkes processes, the proposed method learns the target model iteratively, which generates "easy" sequences and uses them in an adversarial and self-paced manner.
Interpretable ICD Code Embeddings with Self- and Mutual-Attention Mechanisms
The proposed method achieves clinically-interpretable embeddings of ICD codes, and outperforms state-of-the-art embedding methods in procedure recommendation.
Topic-Guided Variational Auto-Encoder for Text Generation
We propose a topic-guided variational auto-encoder (TGVAE) model for text generation.
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
Using this concept, we extend our method to multi-graph partitioning and matching by learning a Gromov-Wasserstein barycenter graph for multiple observed graphs; the barycenter graph plays the role of the disconnected graph, and since it is learned, so is the clustering.
Topic-Guided Variational Autoencoders for Text Generation
We propose a topic-guided variational autoencoder (TGVAE) model for text generation.
Quaternion Convolutional Neural Networks
Neural networks in the real domain have been studied for a long time and achieved promising results in many vision tasks for recent years.
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
A novel Gromov-Wasserstein learning framework is proposed to jointly match (align) graphs and learn embedding vectors for the associated graph nodes.
PoPPy: A Point Process Toolbox Based on PyTorch
The goal of PoPPy is providing a user-friendly solution to the key points above and achieving large-scale point process-based sequential data analysis, simulation and prediction.
Distilled Wasserstein Learning for Word Embedding and Topic Modeling
When learning the topic model, we leverage a distilled underlying distance matrix to update the topic distributions and smoothly calculate the corresponding optimal transports.
Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model
Health risks from cigarette smoking -- the leading cause of preventable death in the United States -- can be substantially reduced by quitting.
Learning an Inverse Tone Mapping Network with a Generative Adversarial Regularizer
Transferring a low-dynamic-range (LDR) image to a high-dynamic-range (HDR) image, which is the so-called inverse tone mapping (iTM), is an important imaging technique to improve visual effects of imaging devices.
Superposition-Assisted Stochastic Optimization for Hawkes Processes
We consider the learning of multi-agent Hawkes processes, a model containing multiple Hawkes processes with shared endogenous impact functions and different exogenous intensities.
Visually Explainable Recommendation
By this, we can not only provide recommendation results to the users, but also tell the users why an item is recommended by providing intuitive visual highlights in a personalized manner.
A unified framework for manifold landmarking
Active manifold learning aims to select and label representative landmarks on a manifold from a given set of samples to improve semi-supervised manifold learning.
Benefits from Superposed Hawkes Processes
The superposition of Hawkes processes is demonstrated to be beneficial for tightening the upper bound of excess risk under certain conditions, and we show the feasibility of the benefit in typical situations.
Learning Registered Point Processes from Idiosyncratic Observations
A parametric point process model is developed, with modeling based on the assumption that sequential observations often share latent phenomena, while also possessing idiosyncratic effects.
Flexible Network Binarization with Layer-wise Priority
In this work, we reveal an important fact that binarizing different layers has a widely-varied effect on the compression ratio of network and the loss of performance.
THAP: A Matlab Toolkit for Learning with Hawkes Processes
As a powerful tool of asynchronous event sequence analysis, point processes have been studied for a long time and achieved numerous successes in different fields.
Learning Hawkes Processes from Short Doubly-Censored Event Sequences
Many real-world applications require robust algorithms to learn point processes based on a type of incomplete data --- the so-called short doubly-censored (SDC) event sequences.
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering
We propose an effective method to solve the event sequence clustering problems based on a novel Dirichlet mixture model of a special but significant type of point processes --- Hawkes process.
A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories
Our approach first leverages the complete information from given trajectories to construct a thermal transfer field which provides a context-rich way to describe the global motion pattern in a scene.
Fractal Dimension Invariant Filtering and Its CNN-based Implementation
By adding a nonlinear post-processing step behind anisotropic filter banks, we demonstrate that the proposed filtering method is capable of preserving the local invariance of the fractal dimension of image.
Learning Granger Causality for Hawkes Processes
In this paper, we propose an effective method, learning Granger causality, for a special but significant type of point processes --- Hawkes process.
Patient Flow Prediction via Discriminative Learning of Mutually-Correcting Processes
By treating a sequence of transition events as a point process, we develop a novel framework for modeling patient flow through various CUs and jointly predicting patients' destination CUs and duration days.
A Matrix Decomposition Perspective to Multiple Graph Matching
Graph matching has a wide spectrum of real-world applications and in general is known NP-hard.
Unsupervised Trajectory Clustering via Adaptive Multi-Kernel-Based Shrinkage
Facing to the challenges of trajectory clustering, e. g., large variations within a cluster and ambiguities across clusters, we first introduce an adaptive multi-kernel-based estimation process to estimate the `shrunk' positions and speeds of trajectories' points.
Manifold Based Dynamic Texture Synthesis from Extremely Few Samples
In this paper, we present a novel method to synthesize dynamic texture sequences from extremely few samples, e. g., merely two possibly disparate frames, leveraging both Markov Random Fields (MRFs) and manifold learning.
