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Found 52 papers, 19 papers with code
Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical solution to transfer knowledge from a label-rich source graph to a completely unlabelled target graph.
HyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional Embedding
Existing knowledge hypergraph embedding methods mainly focused on improving model performance, but their model structures are becoming more complex and redundant.
ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion
In this paper, we propose a novel dynamic convolutional embedding model ConvD for knowledge graph completion, which directly reshapes the relation embeddings into multiple internal convolution kernels to improve the external convolution kernels of the traditional convolutional embedding model.
Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection
To address these issues, we utilize the advantages of reinforcement learning in adaptively learning in complex environments and propose a novel method that incorporates Reinforcement neighborhood selection for unsupervised graph ANomaly Detection (RAND).
An empirical study of next-basket recommendations
This methodological rigor establishes a cohesive framework for the impartial evaluation of diverse NBR approaches.
CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks
Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world.
Give Us the Facts: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling
Recently, ChatGPT, a representative large language model (LLM), has gained considerable attention due to its powerful emergent abilities.
Cold-Start based Multi-Scenario Ranking Model for Click-Through Rate Prediction
Online travel platforms (OTPs), e. g., Ctrip. com or Fliggy. com, can effectively provide travel-related products or services to users.
GIPA: A General Information Propagation Algorithm for Graph Learning
Specifically, the bit-wise correlation calculates the element-wise attention weight through a multi-layer perceptron (MLP) based on the dense representations of two nodes and their edge; The feature-wise correlation is based on the one-hot representations of node attribute features for feature selection.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Context-Aware Robust Fine-Tuning
Contrastive Language-Image Pre-trained (CLIP) models have zero-shot ability of classifying an image belonging to "[CLASS]" by using similarity between the image and the prompt sentence "a [CONTEXT] of [CLASS]".
Ranked #1 on
Domain Generalization
on VLCS
Oscillatory cooperation prevalence emerges from misperception
Here we show that oscillatory behaviors naturally emerge if incomplete information is incorporated into the cooperation evolution of a non-Markov model.
A Systematical Evaluation for Next-Basket Recommendation Algorithms
Different studies often evaluate NBR approaches on different datasets, under different experimental settings, making it hard to fairly and effectively compare the performance of different NBR approaches.
Defending Against Backdoor Attack on Graph Nerual Network by Explainability
After identifying the malicious sample, the explainability of the GNN model can help us capture the most significant subgraph which is probably the trigger in a trojan graph.
Path-aware Siamese Graph Neural Network for Link Prediction
In this paper, we propose a Path-aware Siamese Graph neural network(PSG) for link prediction tasks.
Ranked #1 on
Link Property Prediction
on ogbl-ddi
Re-weighting Negative Samples for Model-Agnostic Matching
Recommender Systems (RS), as an efficient tool to discover users' interested items from a very large corpus, has attracted more and more attention from academia and industry.
A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions
Motivated by the tremendous success of deep learning in clustering, one of the most fundamental machine learning tasks, and the large number of recent advances in this direction, in this paper we conduct a comprehensive survey on deep clustering by proposing a new taxonomy of different state-of-the-art approaches.
RMGN: A Regional Mask Guided Network for Parser-free Virtual Try-on
Virtual try-on(VTON) aims at fitting target clothes to reference person images, which is widely adopted in e-commerce. Existing VTON approaches can be narrowly categorized into Parser-Based(PB) and Parser-Free(PF) by whether relying on the parser information to mask the persons' clothes and synthesize try-on images.
Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump
With the proliferation of pump-and-dump schemes (P&Ds) in the cryptocurrency market, it becomes imperative to detect such fraudulent activities in advance to alert potentially susceptible investors.
MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation
We integrate these components into a unified framework and present MAMDR, which can be applied to any model structure to perform multi-domain recommendation.
Community Trend Prediction on Heterogeneous Graph in E-commerce
In online shopping, ever-changing fashion trends make merchants need to prepare more differentiated products to meet the diversified demands, and e-commerce platforms need to capture the market trend with a prophetic vision.
GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction
Specifically, we establish a heterogeneous graph that contains physical and semantic linkages to guide the feature transfer process from warmed-up video to cold-start videos.
Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation
In many classical e-commerce platforms, personalized recommendation has been proven to be of great business value, which can improve user satisfaction and increase the revenue of platforms.
Graph-Based Stock Recommendation by Time-Aware Relational Attention Network
For a given group of stocks, the proposed TRAN model can output the ranking results of stocks according to their return ratios.
DBC-Forest: Deep forest with binning confidence screening
As a deep learning model, deep confidence screening forest (gcForestcs) has achieved great success in various applications.
GraphPAS: Parallel Architecture Search for Graph Neural Networks
In this paper, we propose a parallel graph architecture search (GraphPAS) framework for graph neural networks.
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming
To overcome the aforementioned problems, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme.
Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting
AMF-STGCN extends GCN by (1) jointly modeling the complex spatial-temporal dependencies in mobile networks, (2) applying attention mechanisms to capture various Receptive Fields of heterogeneous base stations, and (3) introducing an extra decoder based on a fully connected deep network to conquer the error propagation challenge with multi-step forecasting.
Pre-trained Language Models in Biomedical Domain: A Systematic Survey
In this paper, we summarize the recent progress of pre-trained language models in the biomedical domain and their applications in biomedical downstream tasks.
Thompson Sampling for Unimodal Bandits
In this paper, we propose a Thompson Sampling algorithm for \emph{unimodal} bandits, where the expected reward is unimodal over the partially ordered arms.
Physical Artificial Intelligence: The Concept Expansion of Next-Generation Artificial Intelligence
In this paper we explore the concept of Physicial Artifical Intelligence and propose two subdomains: Integrated Physicial Artifical Intelligence and Distributed Physicial Artifical Intelligence.
GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy
Herein, we present GraphTheta, the first distributed and scalable graph learning system built upon vertex-centric distributed graph processing with neural network operators implemented as user-defined functions.
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way.
Reduction of planar double-box diagram for single-top production via auxiliary mass flow
The single-top production is an important process at the LHC to test the Standard Model (SM) and search for the new physics beyond the SM.
High Energy Physics - Phenomenology
Adaptive Spatial-Temporal Inception Graph Convolutional Networks for Multi-step Spatial-Temporal Network Data Forecasting
Spatial-temporal data forecasting is of great importance for industries such as telecom network operation and transportation management.
Categorization of two-loop Feynman diagrams in the $\mathcal O(α^2)$ correction to $e^+e^- \rightarrow ZH$
The $e^+e^- \rightarrow ZH$ process is the dominant process for the Higgs boson production at the future Higgs factory.
High Energy Physics - Phenomenology
Cyclic Label Propagation for Graph Semi-supervised Learning
To overcome these limitations, we introduce a novel framework for graph semi-supervised learning termed as Cyclic Label Propagation (CycProp for abbreviation), which integrates GNNs into the process of label propagation in a cyclic and mutually reinforcing manner to exploit the advantages of both GNNs and LPA.
Distant Supervision for E-commerce Query Segmentation via Attention Network
The booming online e-commerce platforms demand highly accurate approaches to segment queries that carry the product requirements of consumers.
Unsupervised Cross-Lingual Adaptation of Dependency Parsers Using CRF Autoencoders
We consider the task of cross-lingual adaptation of dependency parsers without annotated target corpora and parallel corpora.
Method and Dataset Entity Mining in Scientific Literature: A CNN + Bi-LSTM Model with Self-attention
We finally apply our model on PAKDD papers published from 2009-2019 to mine insightful results from scientific papers published in a longer time span.
Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review
We show the importance and feasibility of learning comprehensive representations of patient EHR data through a systematic review.
Demon: Improved Neural Network Training with Momentum Decay
Momentum is a widely used technique for gradient-based optimizers in deep learning.
Cross-modal Zero-shot Hashing
It then defines an objective function to achieve deep feature learning compatible with the composite similarity preserving, category attribute space learning, and hashing coding function learning.
ActiveHNE: Active Heterogeneous Network Embedding
To maximize the profit of utilizing the rare and valuable supervised information in HNEs, we develop a novel Active Heterogeneous Network Embedding (ActiveHNE) framework, which includes two components: Discriminative Heterogeneous Network Embedding (DHNE) and Active Query in Heterogeneous Networks (AQHN).
Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics
In this area, extracting effective statistical characteristics from a JPEG image for classification remains a challenge.
Personalized Bundle List Recommendation
In particular, BGN improves the precision of the best competitors by 16\% on average while maintaining the highest diversity on four datasets, and yields a 3. 85x improvement of response time over the best competitors in the bundle list recommendation problem.
Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce
To address this discrepancy, previous studies mainly consider textual information of long product titles and lacks of human-like view during training and evaluation process.
Product Title Refinement via Multi-Modal Generative Adversarial Learning
Nowadays, an increasing number of customers are in favor of using E-commerce Apps to browse and purchase products.
Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction
Then, it is possible to utilize unlabeled data that have a potential of failure to further improve the performance of the model.
Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant
Slot filling is a critical task in natural language understanding (NLU) for dialog systems.
Automatic Generation of Chinese Short Product Titles for Mobile Display
This paper studies the problem of automatically extracting a short title from a manually written longer description of E-commerce products for display on mobile devices.
Tournament selection in zeroth-level classifier systems based on average reward reinforcement learning
As a genetics-based machine learning technique, zeroth-level classifier system (ZCS) is based on a discounted reward reinforcement learning algorithm, bucket-brigade algorithm, which optimizes the discounted total reward received by an agent but is not suitable for all multi-step problems, especially large-size ones.
New parton distributions for collider physics
We extract new parton distribution functions (PDFs) of the proton by global analysis of hard scattering data in the general-mass framework of perturbative quantum chromodynamics.
High Energy Physics - Phenomenology
