Search Results for author:
Found 133 papers, 61 papers with code
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning.
Ranked #1 on
Time Series Forecasting
on ETTh2 (336) Univariate
Predicting Temporal Sets with Deep Neural Networks
Given a sequence of sets, where each set contains an arbitrary number of elements, the problem of temporal sets prediction aims to predict the elements in the subsequent set.
A Comprehensive Survey on Transfer Learning
In order to show the performance of different transfer learning models, over twenty representative transfer learning models are used for experiments.
Deep Subdomain Adaptation Network for Image Classification
The adaptation can be achieved easily with most feed-forward network models by extending them with LMMD loss, which can be trained efficiently via back-propagation.
Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction
The hierarchical attentive aggregation can capture spatial dependencies among atoms, as well as fuse the position-enhanced information with the capability of discriminating multiple spatial relations among atoms.
A Survey on Large Language Models for Recommendation
Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS).
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model scopes, and application areas/tasks.
A Survey of Reasoning with Foundation Models
Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation.
Multi-Temporal Relationship Inference in Urban Areas
Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL).
Spatial Heterophily Aware Graph Neural Networks
However, urban graphs usually can be observed to possess a unique spatial heterophily property; that is, the dissimilarity of neighbors at different spatial distances can exhibit great diversity.
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning
In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection.
Decode Neural signal as Speech
In this paper, we explore the brain-to-text translation of MEG signals in a speech-decoding formation.
LLMLight: Large Language Models as Traffic Signal Control Agents
Traffic Signal Control (TSC) is a crucial component in urban traffic management, aiming to optimize road network efficiency and reduce congestion.
Dynamic Word Embeddings for Evolving Semantic Discovery
Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution.
Priors in Deep Image Restoration and Enhancement: A Survey
Image restoration and enhancement is a process of improving the image quality by removing degradations, such as noise, blur, and resolution degradation.
UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction
We hope the proposed UUKG fosters research on urban knowledge graphs and broad smart city applications.
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.
Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models
Machine learning techniques are now integral to the advancement of intelligent urban services, playing a crucial role in elevating the efficiency, sustainability, and livability of urban environments.
EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction
In EERNN, we simply summarize each student's state into an integrated vector and trace it with a recurrent neural network, where we design a bidirectional LSTM to learn the encoding of each exercise's content.
Heterogeneous Graph Representation Learning with Relation Awareness
Moreover, a semantic fusing module is presented to aggregate relation-aware node representations into a compact representation with the learned relation representations.
Ranked #21 on
Node Property Prediction
on ogbn-mag
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.
Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning
Electric Vehicle (EV) has become a preferable choice in the modern transportation system due to its environmental and energy sustainability.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction
However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e. g., camera, ultrasonic sensor, and GPS).
Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity
To this end, we propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool).
Ranked #3 on
Protein-Ligand Affinity Prediction
on PDBbind
Learning to Walk with Dual Agents for Knowledge Graph Reasoning
Graph walking based on reinforcement learning (RL) has shown great success in navigating an agent to automatically complete various reasoning tasks over an incomplete knowledge graph (KG) by exploring multi-hop relational paths.
Survey on Trustworthy Graph Neural Networks: From A Causal Perspective
Moreover, we introduce a taxonomy of Causality-Inspired GNNs (CIGNNs) based on the type of causal learning capability they are equipped with, i. e., causal reasoning and causal representation learning.
MetAug: Contrastive Learning via Meta Feature Augmentation
We perform a meta learning technique to build the augmentation generator that updates its network parameters by considering the performance of the encoder.
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.
GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification
Class imbalance is the phenomenon that some classes have much fewer instances than others, which is ubiquitous in real-world graph-structured scenarios.
Out-of-Town Recommendation with Travel Intention Modeling
Second, a user-specific travel intention is formulated as an aggregation combining home-town preference and generic travel intention together, where the generic travel intention is regarded as a mixture of inherent intentions that can be learned by Neural Topic Model (NTM).
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction
Though graph contrastive learning (GCL) methods have achieved extraordinary performance with insufficient labeled data, most focused on designing data augmentation schemes for general graphs.
Topic Modeling Revisited: A Document Graph-based Neural Network Perspective
To this end, in this paper, we revisit the task of topic modeling by transforming each document into a directed graph with word dependency as edges between word nodes, and develop a novel approach, namely Graph Neural Topic Model (GNTM).
Cross-modality Data Augmentation for End-to-End Sign Language Translation
It has been a challenging task due to the modality gap between sign videos and texts and the data scarcity of labeled data.
Ranked #3 on
Sign Language Translation
on CSL-Daily
HomoGCL: Rethinking Homophily in Graph Contrastive Learning
Contrastive learning (CL) has become the de-facto learning paradigm in self-supervised learning on graphs, which generally follows the "augmenting-contrasting" learning scheme.
DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load
Accordingly, we devise a novel Diffusion model termed DiffPLF for Probabilistic Load Forecasting of EV charging, which can explicitly approximate the predictive load distribution conditioned on historical data and related covariates.
MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning
As a successful approach to self-supervised learning, contrastive learning aims to learn invariant information shared among distortions of the input sample.
Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph
However, existing HGNNs usually represent each node as a single vector in the multi-layer graph convolution calculation, which makes the high-level graph convolution layer fail to distinguish information from different relations and different orders, resulting in the information loss in the message passing.
SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback
The recent development of online recommender systems has a focus on collaborative ranking from implicit feedback, such as user clicks and purchases.
Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning
Representation learning on heterogeneous graphs aims to obtain low-dimensional node representations that could preserve both node attributes and relation information.
Ranked #23 on
Node Property Prediction
on ogbn-mag
DiffCharge: Generating EV Charging Scenarios via a Denoising Diffusion Model
Recent proliferation of electric vehicle (EV) charging events has brought prominent stress over power grid operation.
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences
It is critical and important to detect anomalies in event sequences, which becomes widely available in many application domains. In-deed, various efforts have been made to capture abnormal patterns from event sequences through sequential pattern analysis or event representation learning. However, existing approaches usually ignore the semantic information of event content. To this end, in this paper, we propose a self-attentive encoder-decoder transformer framework, Content-Aware Transformer(CAT), for anomaly detection in event sequences. In CAT, the encoder learns preamble event sequence representations with content awareness, and the decoder embeds sequences under detection into a latent space, where anomalies are distinguishable. Specifically, the event content is first fed to a content-awareness layer, generating representations of each event. The encoder accepts preamble event representation sequence, generating feature maps. In the decoder, an additional token is added at the beginning of the sequence under detection, denoting the sequence status. A one-class objective together with sequence reconstruction loss is collectively applied to train our framework under the label efficiency scheme. Furthermore, CAT is optimized under a scalable and efficient setting. Finally, extensive experiments on three real-world datasets demonstrate the superiority of CAT.
LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay
To achieve this goal, we adopt Avalon, a representative communication game, as the environment and use system prompts to guide LLM agents to play the game.
Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach
Although remarkable progress on the neural table-to-text methods has been made, the generalization issues hinder the applicability of these models due to the limited source tables.
Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness
As one of the most popular generative models, Variational Autoencoder (VAE) approximates the posterior of latent variables based on amortized variational inference.
Towards Task Sampler Learning for Meta-Learning
We obtain three conclusions: (i) there is no universal task sampling strategy that can guarantee the optimal performance of meta-learning models; (ii) over-constraining task diversity may incur the risk of under-fitting or over-fitting during training; and (iii) the generalization performance of meta-learning models are affected by task diversity, task entropy, and task difficulty.
How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model Ranking
This paper presents LEMR (Label-Efficient Model Ranking) and introduces the MoraBench Benchmark.
Hyperbolic Graph Neural Networks: A Review of Methods and Applications
Graph neural networks generalize conventional neural networks to graph-structured data and have received widespread attention due to their impressive representation ability.
Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity
To this end, we propose a methodology, specifically consistency and complementarity network (CoCoNet), which avails of strict global inter-view consistency and local cross-view complementarity preserving regularization to comprehensively learn representations from multiple views.
Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation
To this end, in this paper, we aim to discern the decision-making processes of neural networks through a hierarchical voting strategy by developing an explainable deep learning model, namely Voting Transformation-based Explainable Neural Network (VOTEN).
TriMLP: Revenge of a MLP-like Architecture in Sequential Recommendation
In this paper, we present a MLP-like architecture for sequential recommendation, namely TriMLP, with a novel Triangular Mixer for cross-token communications.
Graph Signal Diffusion Model for Collaborative Filtering
In this paper, we make novel adaptions of diffusion model and propose Graph Signal Diffusion Model for Collaborative Filtering (named GiffCF).
Event Camera Demosaicing via Swin Transformer and Pixel-focus Loss
To end this, we present a Swin-Transformer-based backbone and a pixel-focus loss function for demosaicing with missing pixel values in RAW domain processing.
Intriguing Properties of Positional Encoding in Time Series Forecasting
Motivated by these findings, we introduce two new PEs: Temporal Position Encoding (T-PE) for temporal tokens and Variable Positional Encoding (V-PE) for variable tokens.
Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems
Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility.
Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective
Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features.
BayesPrompt: Prompting Large-Scale Pre-Trained Language Models on Few-shot Inference via Debiased Domain Abstraction
From the perspective of distribution analyses, we disclose that the intrinsic issues behind the phenomenon are the over-multitudinous conceptual knowledge contained in PLMs and the abridged knowledge for target downstream domains, which jointly result in that PLMs mis-locate the knowledge distributions corresponding to the target domains in the universal knowledge embedding space.
AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms.
Risk-Averse Classification
We develop a new approach to solving classification problems, which is bases on the theory of coherent measures of risk and risk sharing ideas.
Recruitment Market Trend Analysis with Sequential Latent Variable Models
To this end, in this paper, we propose a new research paradigm for recruitment market analysis by leveraging unsupervised learning techniques for automatically discovering recruitment market trends based on large-scale recruitment data.
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.
Seeing the Forest from the Trees in Two Looks: Matrix Sketching by Cascaded Bilateral Sampling
Given a matrix A of size m by n, state-of-the-art randomized algorithms take O(m * n) time and space to obtain its low-rank decomposition.
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.
Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning
To this end, in this paper, we propose a novel end-to-end data-driven model based on Convolutional Neural Network (CNN), namely Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to the requirements of a job.
FineFool: Fine Object Contour Attack via Attention
Inspired by the correlations between adversarial perturbations and object contour, slighter perturbations is produced via focusing on object contour features, which is more imperceptible and difficult to be defended, especially network add-on defense methods with the trade-off between perturbations filtering and contour feature loss.
Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach
The wide spread use of online recruitment services has led to information explosion in the job market.
Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation
Then, we integrate the aesthetic features into a cross-domain network to transfer users' domain independent aesthetic preferences.
POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm
In this paper, comprehensive evaluation metrics are brought up for different adversarial attack methods.
STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control
Moreover, based on the temporally-dependent traffic information, we design a Graph Neural Network based model to represent relationships among multiple traffic lights, and the decision for each traffic light will be made in a distributed way by the deep Q-learning method.
A Machine Learning-enhanced Robust P-Phase Picker for Real-time Seismic Monitoring
To this end, in this paper, we present a machine learning-enhanced framework based on ensemble learning strategy, EL-Picker, for the automatic identification of seismic P-phase arrivals on continuous and massive waveforms.
Comprehensive and Efficient Data Labeling via Adaptive Model Scheduling
With limited computing resources and stringent delay, given a data stream and a collection of applicable resource-hungry deep-learning models, we design a novel approach to adaptively schedule a subset of these models to execute on each data item, aiming to maximize the value of the model output (e. g., the number of high-confidence labels).
A Survey on Knowledge Graph-Based Recommender Systems
On the one hand, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation.
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.
Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning
A mobile app interface usually consists of a set of user interface modules.
S2OSC: A Holistic Semi-Supervised Approach for Open Set Classification
Open set classification (OSC) tackles the problem of determining whether the data are in-class or out-of-class during inference, when only provided with a set of in-class examples at training time.
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.
E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce
Pre-trained language models such as BERT have achieved great success in a broad range of natural language processing tasks.
Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning
However, it is still a challenging task since (1) the job title and job transition (job-hopping) data is messy which contains a lot of subjective and non-standard naming conventions for the same position (e. g., Programmer, Software Development Engineer, SDE, Implementation Engineer), (2) there is a large amount of missing title/transition information, and (3) one talent only seeks limited numbers of jobs which brings the incompleteness and randomness modeling job transition patterns.
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).
T$^2$-Net: A Semi-supervised Deep Model for Turbulence Forecasting
Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs.
Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks
Specifically, we first propose a heterogeneous recurrent graph neural network to model the spatiotemporal autocorrelation among air quality and weather monitoring stations.
Spatial Object Recommendation with Hints: When Spatial Granularity Matters
We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level.
CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation
Electric vehicles have been rapidly increasing in usage, but stations to charge them have not always kept up with demand, so efficient routing of vehicles to stations is critical to operating at maximum efficiency.
Graphfool: Targeted Label Adversarial Attack on Graph Embedding
To the best of our knowledge, this is the first targeted label attack technique.
Joint Intent Detection and Entity Linking on Spatial Domain Queries
Continuous efforts have been devoted to language understanding (LU) for conversational queries with the fast and wide-spread popularity of voice assistants.
MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal
Real estate appraisal refers to the process of developing an unbiased opinion for real property's market value, which plays a vital role in decision-making for various players in the marketplace (e. g., real estate agents, appraisers, lenders, and buyers).
Information Theory-Guided Heuristic Progressive Multi-View Coding
To this end, we rethink the existing multi-view learning paradigm from the information theoretical perspective and then propose a novel information theoretical framework for generalized multi-view learning.
Adversarial Neural Trip Recommendation
To that end, we propose an Adversarial Neural Trip Recommendation (ANT) framework to tackle the above challenges.
Domain-Invariant Representation Learning with Global and Local Consistency
In this paper, we give an analysis of the existing representation learning framework of unsupervised domain adaptation and show that the learned feature representations of the source domain samples are with discriminability, compressibility, and transferability.
Exploiting Cross-Modal Prediction and Relation Consistency for Semi-Supervised Image Captioning
In detail, considering that the heterogeneous gap between modalities always leads to the supervision difficulty of using the global embedding directly, CPRC turns to transform both the raw image and corresponding generated sentence into the shared semantic space, and measure the generated sentence from two aspects: 1) Prediction consistency.
Towards Robust Knowledge Graph Embedding via Multi-task Reinforcement Learning
This will lead to low-quality and unreliable representations of KGs.
Multi-Domain Transformer-Based Counterfactual Augmentation for Earnings Call Analysis
Earnings call (EC), as a periodic teleconference of a publicly-traded company, has been extensively studied as an essential market indicator because of its high analytical value in corporate fundamentals.
Domain-oriented Language Pre-training with Adaptive Hybrid Masking and Optimal Transport Alignment
Also, the word co-occurrences guided semantic learning of pre-training models can be largely augmented by entity-level association knowledge.
Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification
Then, target temporal pattern in combination with user and POI information are fed into a multi-layer network to capture users' dynamic preferences.
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.
Robust Local Preserving and Global Aligning Network for Adversarial Domain Adaptation
We conduct theoretical analysis on the robustness of the proposed RLPGA and prove that the robust informative-theoretic-based loss and the local preserving module are beneficial to reduce the empirical risk of the target domain.
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.
Feature and Instance Joint Selection: A Reinforcement Learning Perspective
Feature selection and instance selection are two important techniques of data processing.
Detect Professional Malicious User with Metric Learning in Recommender Systems
2) the PMU detection model should take both ratings and reviews into consideration, which makes PMU detection a multi-modal problem.
Interventional Contrastive Learning with Meta Semantic Regularizer
Contrastive learning (CL)-based self-supervised learning models learn visual representations in a pairwise manner.
Customized Conversational Recommender Systems
In this paper, we propose two key points for CRS to improve the user experience: (1) Speaking like a human, human can speak with different styles according to the current dialogue context.
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.
A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection
The core idea of such a framework is to firstly pre-train a basis (or master) model over the URG, and then to adaptively derive specific (or slave) models from the basis model for different regions.
Pontryagin Optimal Control via Neural Networks
Solving real-world optimal control problems are challenging tasks, as the complex, high-dimensional system dynamics are usually unrevealed to the decision maker.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
A Survey on Deep Learning based Time Series Analysis with Frequency Transformation
Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT.
Preference or Intent? Double Disentangled Collaborative Filtering
In this way, the low-rating items will be treated as positive samples for modeling intents while the negative samples for modeling preferences.
Towards Language-guided Interactive 3D Generation: LLMs as Layout Interpreter with Generative Feedback
Generating and editing a 3D scene guided by natural language poses a challenge, primarily due to the complexity of specifying the positional relations and volumetric changes within the 3D space.
Manifold Constraint Regularization for Remote Sensing Image Generation
This study identifies a previously overlooked issue: GANs exhibit a heightened susceptibility to overfitting on remote sensing images. To address this challenge, this paper analyzes the characteristics of remote sensing images and proposes manifold constraint regularization, a novel approach that tackles overfitting of GANs on remote sensing images for the first time.
Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction
This paper introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role and a multi-channel argument role prediction module.
Generative Job Recommendations with Large Language Model
The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process.
A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics
To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management.
Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms
Machine learning algorithms have become ubiquitous in a number of applications (e. g. image classification).
Digital twin brain: a bridge between biological intelligence and artificial intelligence
In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities for understanding the complexity of the brain and its emulation by computational systems.
Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network
Accurate traffic forecasting at intersections governed by intelligent traffic signals is critical for the advancement of an effective intelligent traffic signal control system.
Semi-supervised Domain Adaptation in Graph Transfer Learning
To deal with the domain shift, we add adaptive shift parameters to each of the source nodes, which are trained in an adversarial manner to align the cross-domain distributions of node embedding, thus the node classifier trained on labeled source nodes can be transferred to the target nodes.
Towards Faithful Neural Network Intrinsic Interpretation with Shapley Additive Self-Attribution
Self-interpreting neural networks have garnered significant interest in research.
Cloth2Body: Generating 3D Human Body Mesh from 2D Clothing
Finally, how to generate diverse and plausible results from a 2D clothing image.
Machine Learning for Urban Air Quality Analytics: A Survey
In this article, we present a comprehensive survey of ML-based air quality analytics, following a roadmap spanning from data acquisition to pre-processing, and encompassing various analytical tasks such as pollution pattern mining, air quality inference, and forecasting.
Iterative Prediction-and-Optimization for E-Logistics Distribution Network Design
Summary of Contribution: We propose an iterative prediction-and-optimization algorithm for multilevel distribution network design for e-logistics and evaluate its operational value for online retailers.
Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment
One prevalent solution is the bi-discriminator domain adversarial network, which strives to identify target domain samples outside the support of the source domain distribution and enforces their classification to be consistent on both discriminators.
DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops
We demonstrate theoretically that our approach mitigates the negative effects of feedback loops and unknown exposure mechanisms.
The Impact of Generative Artificial Intelligence
The rise of generative artificial intelligence (AI) has sparked concerns about its potential influence on unemployment and market depression.
GeoDeformer: Geometric Deformable Transformer for Action Recognition
Specifically, at the core of GeoDeformer is the Geometric Deformation Predictor, a module designed to identify and quantify potential spatial and temporal geometric deformations within the given video.
A Survey of Text Watermarking in the Era of Large Language Models
Text watermarking algorithms play a crucial role in the copyright protection of textual content, yet their capabilities and application scenarios have been limited historically.
Large Language Models are Not Stable Recommender Systems
With the significant successes of large language models (LLMs) in many natural language processing tasks, there is growing interest among researchers in exploring LLMs for novel recommender systems.
The Bigger the Better? Rethinking the Effective Model Scale in Long-term Time Series Forecasting
Through this work, we advocate a paradigm shift in LTSF, emphasizing the importance to tailor the model to the inherent dynamics of time series data-a timely reminder that in the realm of LTSF, bigger is not invariably better.
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling
Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks.
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation
In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to practical deployment.
DGR: A General Graph Desmoothing Framework for Recommendation via Global and Local Perspectives
Graph Convolutional Networks (GCNs) have become pivotal in recommendation systems for learning user and item embeddings by leveraging the user-item interaction graph's node information and topology.
Harnessing Large Language Models for Text-Rich Sequential Recommendation
Recent advances in Large Language Models (LLMs) have been changing the paradigm of Recommender Systems (RS).
JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer
Moreover, we adopt a two-stage learning strategy for skill-aware recommendation, in which we utilize the skill distribution to guide JD representation learning in the recall stage, and then combine the user profiles for final prediction in the ranking stage.
Enhancing Question Answering for Enterprise Knowledge Bases using Large Language Models
For the generation process, we propose a novel chain of thought (CoT) based fine-tuning method to empower the LLM-based generator to adeptly respond to user questions using retrieved documents.
Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in Surface Electromyographic Signal Analysis
We propose a Short Term Enhancement Module(STEM) which can be easily integrated with various models.
