Search Results for author:
Found 56 papers, 17 papers with code
Disentangled Causal Graph Learning forOnline Unsupervised Root Cause Analysis
In this paper, we propose CORAL, a novel online RCA framework that can automatically trigger the RCA process and incrementally update the RCA model.
Locate and Beamform: Two-dimensional Locating All-neural Beamformer for Multi-channel Speech Separation
Recently, stunning improvements on multi-channel speech separation have been achieved by neural beamformers when direction information is available.
Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning
Temporal knowledge graph (TKG) reasoning aims to predict the future missing facts based on historical information and has gained increasing research interest recently.
Towards Automated Urban Planning: When Generative and ChatGPT-like AI Meets Urban Planning
The two fields of urban planning and artificial intelligence (AI) arose and developed separately.
Data-Centric AI: Deep Generative Differentiable Feature Selection via Discrete Subsetting as Continuous Embedding Space Optimization
We generalize this problem into a deep differentiable feature selection task and propose a new perspective: discrete feature subsetting as continuous embedding space optimization.
Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems
The temporal embeddings are mapped to the new graph as node attributes to form weighted attributed graph.
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting
The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes over time, largely hinders the performance of TSF models.
PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation
Our proposed framework provides a conditional feature extraction module first to extract the coarse yet effective spatiotemporal dependencies from conditional information as the global context prior.
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization
REASON consists of Topological Causal Discovery and Individual Causal Discovery.
Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents
Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML).
Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations
To tackle the challenge, we propose a generic model for enabling the current traffic speed prediction methods to preserve implicit spatial correlations.
MIMO-DBnet: Multi-channel Input and Multiple Outputs DOA-aware Beamforming Network for Speech Separation
Recently, many deep learning based beamformers have been proposed for multi-channel speech separation.
GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation
Graph generative models have broad applications in biology, chemistry and social science.
Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning
The third stage is to leverage multi-attentions to model the zone-zone peer dependencies of the functionality projections to generate grid-level land-use configurations.
Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective
We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items.
Kernel-based Substructure Exploration for Next POI Recommendation
To tackle the above issues, we propose a Kernel-Based Graph Neural Network (KBGNN) for next POI recommendation, which combines the characteristics of both geographical and sequential influences in a collaborative way.
Hierarchical MixUp Multi-label Classification with Imbalanced Interdisciplinary Research Proposals
In response to this question, we propose a hierarchical mixup multiple-label classification framework, which we called H-MixUp.
Automated Urban Planning aware Spatial Hierarchies and Human Instructions
While automated urban planners have been examined, they are constrained because of the following: 1) neglecting human requirements in urban planning; 2) omitting spatial hierarchies in urban planning, and 3) lacking numerous urban plan data samples.
Self-Optimizing Feature Transformation
Feature transformation aims to extract a good representation (feature) space by mathematically transforming existing features.
Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification
Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals.
MIMO-DoAnet: Multi-channel Input and Multiple Outputs DoA Network with Unknown Number of Sound Sources
These algorithms are usually achieved by mapping the multi-channel audio input to the single output (i. e. overall spatial pseudo-spectrum (SPS) of all sources), that is called MISO.
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction
Traffic demand forecasting by deep neural networks has attracted widespread interest in both academia and industry society.
Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting
To equip the graph neural network with a flexible and practical graph structure, in this paper, we investigate how to model the evolutionary and multi-scale interactions of time series.
Iterative Sound Source Localization for Unknown Number of Sources
Sound source localization aims to seek the direction of arrival (DOA) of all sound sources from the observed multi-channel audio.
Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction
We reformulate representation space reconstruction into an interactive process of nested feature generation and selection, where feature generation is to generate new meaningful and explicit features, and feature selection is to eliminate redundant features to control feature sizes.
Semi-supervised Drifted Stream Learning with Short Lookback
In many scenarios, 1) data streams are generated in real time; 2) labeled data are expensive and only limited labels are available in the beginning; 3) real-world data is not always i. i. d.
Feature and Instance Joint Selection: A Reinforcement Learning Perspective
Feature selection and instance selection are two important techniques of data processing.
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
However, the complicated dependencies of the PTS signal on its inherent periodicity as well as the sophisticated composition of various periods hinder the performance of PTS forecasting.
Reinforced Imitative Graph Learning for Mobile User Profiling
The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities.
Who Should Review Your Proposal? Interdisciplinary Topic Path Detection for Research Proposals
After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal.
Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams
An event that a user visits a POI in stream updates the states of both users and geospatial contexts; the agent perceives the updated environment state to make online recommendations.
Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation
However, three major challenges arise: 1) how to define a quantitative land-use configuration?
Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning
Existing studies usually ignore the need of personalized human guidance in planning, and spatial hierarchical structure in planning generation.
RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training
In this paper, we propose a multi-task self-supervised learning-based researcher data pre-training model named RPT.
Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy
In this paper, we propose a single-agent Monte Carlo based reinforced feature selection (MCRFS) method, as well as two efficiency improvement strategies, i. e., early stopping (ES) strategy and reward-level interactive (RI) strategy.
Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning
Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models.
Expert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification
In this paper, we formulate the proposal classification problem into a hierarchical multi-label classification task.
Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective
To solve the problem, we propose an adversarial training strategy to guarantee the robustness of the representation module.
Democratizing Evaluation of Deep Model Interpretability through Consensus
Given any task/dataset, Consensus first obtains the interpretation results using existing tools, e. g., LIME (Ribeiro et al., 2016), for every model in the committee, then aggregates the results from the entire committee and approximates the “ground truth” of interpretations through voting.
Coupled Layer-wise Graph Convolution for Transportation Demand Prediction
Graph Convolutional Network (GCN) has been widely applied in transportation demand prediction due to its excellent ability to capture non-Euclidean spatial dependence among station-level or regional transportation demands.
Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop
In this journal version, we propose a novel interactive and closed-loop architecture to simultaneously model interactive reinforcement learning (IRL) and decision tree feedback (DTF).
Simplifying Reinforced Feature Selection via Restructured Choice Strategy of Single Agent
To address the question, we develop a single-agent reinforced feature selection approach integrated with restructured choice strategy.
Learning Adaptive Embedding Considering Incremental Class
To this end, we propose a Class-Incremental Learning without Forgetting (CILF) framework, which aims to learn adaptive embedding for processing novel class detection and model update in a unified framework.
AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement Learning
Motivated by such a computational dilemma, this study is to develop a novel feature space navigation method.
Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection
To this end, we propose a structured anomaly detection framework to defend WTNs by modeling the spatio-temporal characteristics of cyber attacks in WTNs.
Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning
The integrated hierarchical aggregation module aims to preserve the tree structure by combining GNN with Gated Recurrent Unit to integrate the hierarchical and sequential neighborhood information on the tree structure to node representations.
Reimagining City Configuration: Automated Urban Planning via Adversarial Learning
The objective is then to propose an adversarial learning framework that can automatically generate such tensor for an unplanned area.
Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine
Then, we introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation.
Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach
Therefore, item recommendation and attribute inference have become two main tasks in these platforms.
A Neural Influence Diffusion Model for Social Recommendation
The key idea of our proposed model is that we design a layer-wise influence propagation structure to model how users' latent embeddings evolve as the social diffusion process continues.
SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation
Based on a classical CF model, the key idea of our proposed model is that we borrow the strengths of GCNs to capture how users' preferences are influenced by the social diffusion process in social networks.
A Hierarchical Attention Model for Social Contextual Image Recommendation
After that, we design a hierarchical attention network that naturally mirrors the hierarchical relationship (elements in each aspects level, and the aspect level) of users' latent interests with the identified key aspects.
BL-MNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder
The closeness among users in the networks are defined as the meta proximity scores, which will be fed into DIME to learn the embedding vectors of users in the emerging network.
Social and Information Networks Databases
CSWA: Aggregation-Free Spatial-Temporal Community Sensing
In this paper, we present a novel community sensing paradigm -- {C}ommunity {S}ensing {W}ithout {A}ggregation}.
REMIX: Automated Exploration for Interactive Outlier Detection
This formulation incorporates multiple aspects such as (i) an upper limit on the total execution time of detectors (ii) diversity in the space of algorithms and features, and (iii) meta-learning for evaluating the cost and utility of detectors.
Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval
Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes.
