Search Results for author:
Found 84 papers, 27 papers with code
Selection and Generation: Learning towards Multi-Product Advertisement Post Generation
(2) generate a post including selected products via the MGenNet (Multi-Generator Network).
Graph Disentangle Causal Model: Enhancing Causal Inference in Networked Observational Data
GDC utilizes a causal disentangle module to separate unit features into adjustment and confounder representations.
Beyond Feature Mapping GAP: Integrating Real HDRTV Priors for Superior SDRTV-to-HDRTV Conversion
However, the limited information in SDRTV and the diversity of styles in real-world conversions render this process an ill-posed problem, thereby constraining the performance and generalization of these methods.
MKGL: Mastery of a Three-Word Language
Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks.
KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation
The recently developed retrieval-augmented generation (RAG) technology has enabled the efficient construction of domain-specific applications.
OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System
Knowledge representation has been a central aim of AI since its inception.
OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs
This paper introduces a novel and efficient One-pass Generation and retrieval framework (OneGen), designed to improve LLMs' performance on tasks that require both generation and retrieval.
KAN-RCBEVDepth: A multi-modal fusion algorithm in object detection for autonomous driving
Accurate 3D object detection in autonomous driving is critical yet challenging due to occlusions, varying object sizes, and complex urban environments.
Optimizing Long-tailed Link Prediction in Graph Neural Networks through Structure Representation Enhancement
After knowing the weakness of link prediction, a natural question is how can we eliminate the negative effects of the skewed long-tailed distribution on common neighbors so as to improve the performance of link prediction?
Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach
Multi-hop question answering is a challenging task with distinct industrial relevance, and Retrieval-Augmented Generation (RAG) methods based on large language models (LLMs) have become a popular approach to tackle this task.
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs
LLMs fine-tuned with this data have improved planning capabilities, better equipping them to handle complex QA tasks that involve retrieval.
Personalized Binomial DAGs Learning with Network Structured Covariates
Causal discovery aims to recover the DAG structure using observational data.
Efficient Knowledge Infusion via KG-LLM Alignment
To tackle the problem of domain-specific knowledge scarcity within large language models (LLMs), knowledge graph-retrievalaugmented method has been proven to be an effective and efficient technique for knowledge infusion.
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
In recent years, large language models (LLMs) have made remarkable achievements in various domains.
Your decision path does matter in pre-training industrial recommenders with multi-source behaviors
Online service platforms offering a wide range of services through miniapps have become crucial for users who visit these platforms with clear intentions to find services they are interested in.
Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Tasks
In response to the lack of open-source benchmarks, we constructed a new multi-domain KGP benchmark called KPI with two large-scale KGs and six different sub-domain tasks to evaluate our method and open-sourced it for subsequent research.
Adaptive Convolutional Forecasting Network Based on Time Series Feature-Driven
Specifically, by extracting and fusing time series features at different resolutions, we capture both local contextual information and global patterns in the time series.
HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning
In this paper, we propose a neural network model to address multiple tasks jointly upon the input of 3D protein structures.
Continual Few-shot Event Detection via Hierarchical Augmentation Networks
The CFED task is challenging as it involves memorizing previous event types and learning new event types with few-shot samples.
Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm
Applying such paradigm, we construct an evaluation benchmark in the field of urology, including a LCP, a SPs dataset, and an automated RAE.
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation
Subsequently, we employ self-distillation to facilitate the transfer of knowledge from users with extensive behaviors (teachers) to users with limited behaviors (students).
Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling
The estimator is general for all types of outcomes, and is able to comprehensively model the treatment and control group data together to approach the uplift.
Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning
In this paper, we fill in this gap by proposing a motif-preserving Graph Neural Network with curriculum learning (MotifGNN) to jointly learn the lower-order structures from the original graph and higherorder structures from multi-view motif-based graphs for financial default prediction.
ChatUIE: Exploring Chat-based Unified Information Extraction using Large Language Models
This motivated us to explore domain-specific modeling in chat-based language models as a solution for extracting structured information from natural language.
Can Small Language Models be Good Reasoners for Sequential Recommendation?
We encode the generated rationales from the student model into a dense vector, which empowers recommendation in both ID-based and ID-agnostic scenarios.
Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning
Event Causality Identification (ECI) refers to the detection of causal relations between events in texts.
Non-autoregressive Generative Models for Reranking Recommendation
The generator is of vital importance, and generative models are well-suited for the generator function.
MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction
The stock market is a crucial component of the financial system, but predicting the movement of stock prices is challenging due to the dynamic and intricate relations arising from various aspects such as economic indicators, financial reports, global news, and investor sentiment.
A Comprehensive Study of Knowledge Editing for Large Language Models
In this paper, we first define the knowledge editing problem and then provide a comprehensive review of cutting-edge approaches.
Ranked #1 on
knowledge editing
on zsRE
(using extra training data)
RJUA-QA: A Comprehensive QA Dataset for Urology
We introduce RJUA-QA, a novel medical dataset for question answering (QA) and reasoning with clinical evidence, contributing to bridge the gap between general large language models (LLMs) and medical-specific LLM applications.
Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation
In this paper, we seek to carefully prompt a Large Language Model (LLM) with domain-level knowledge as a better marketing-oriented knowledge miner for marketing-oriented knowledge graph construction, which is however non-trivial, suffering from several inevitable issues in real-world marketing scenarios, i. e., uncontrollable relation generation of LLMs, insufficient prompting ability of a single prompt, the unaffordable deployment cost of LLMs.
Not All Negatives Are Worth Attending to: Meta-Bootstrapping Negative Sampling Framework for Link Prediction
In particular, we elaborately devise a Meta-learning Supported Teacher-student GNN (MST-GNN) that is not only built upon teacher-student architecture for alleviating the migration between "easy" and "hard" samples but also equipped with a meta learning based sample re-weighting module for helping the student GNN distinguish "hard" samples in a fine-grained manner.
PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation
To help merchants/customers to provide/access a variety of services through miniapps, online service platforms have occupied a critical position in the effective content delivery, in which how to recommend items in the new domain launched by the service provider for customers has become more urgent.
Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework
In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspects in a disentangled manner.
Think-in-Memory: Recalling and Post-thinking Enable LLMs with Long-Term Memory
Memory-augmented Large Language Models (LLMs) have demonstrated remarkable performance in long-term human-machine interactions, which basically relies on iterative recalling and reasoning of history to generate high-quality responses.
Long-tail Augmented Graph Contrastive Learning for Recommendation
To make the data augmentation schema learnable, we design an auto drop module to generate pseudo-tail nodes from head nodes and a knowledge transfer module to reconstruct the head nodes from pseudo-tail nodes.
BATINet: Background-Aware Text to Image Synthesis and Manipulation Network
We proposed a Background-Aware Text to Image synthesis and manipulation Network (BATINet), which contains two key components: Position Detect Network (PDN) and Harmonize Network (HN).
Generative Contrastive Graph Learning for Recommendation
Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph.
A Physics-Informed Low-Shot Learning For sEMG-Based Estimation of Muscle Force and Joint Kinematics
This method seamlessly integrates Lagrange's equation of motion and inverse dynamic muscle model into the generative adversarial network (GAN) framework for structured feature decoding and extrapolated estimation from the small sample data.
Dual Inverse Degradation Network for Real-World SDRTV-to-HDRTV Conversion
In this study, we address the emerging necessity of converting Standard Dynamic Range Television (SDRTV) content into High Dynamic Range Television (HDRTV) in light of the limited number of native HDRTV content.
InferTurbo: A Scalable System for Boosting Full-graph Inference of Graph Neural Network over Huge Graphs
Inspired by the philosophy of ``think-like-a-vertex", a GAS-like (Gather-Apply-Scatter) schema is proposed to describe the computation paradigm and data flow of GNN inference.
Differential Privacy May Have a Potential Optimization Effect on Some Swarm Intelligence Algorithms besides Privacy-preserving
For this reason, this paper attempts to combine DP and SI for the first time, and proposes a general differentially private swarm intelligence algorithm framework (DPSIAF).
A Fast Fourier Convolutional Deep Neural Network For Accurate and Explainable Discrimination Of Wheat Yellow Rust And Nitrogen Deficiency From Sentinel-2 Time-Series Data
Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress.
Description-Enhanced Label Embedding Contrastive Learning for Text Classification
Then, we propose a novel Relation of Relation Learning Network (R2-Net) for text classification, in which text classification and R2 classification are treated as optimization targets.
Who Would be Interested in Services? An Entity Graph Learning System for User Targeting
At the online stage, the system offers the ability of user targeting in real-time based on the entity graph from the offline stage.
COUPA: An Industrial Recommender System for Online to Offline Service Platforms
Aiming at helping users locally discovery retail services (e. g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which greatly challenge current recommender systems.
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning
At last, we conduct extensive experiments on both offline and online environments, which demonstrates the superior capability of GARCIA in improving tail queries and overall performance in service search scenarios.
Adversarially Robust Neural Architecture Search for Graph Neural Networks
To tackle these challenges, we propose a novel Robust Neural Architecture search framework for GNNs (G-RNA).
Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning
We evaluate our method both in closed-loop simulation and real world driving, and demonstrate the neural network planner has outstanding performance in complex urban autonomous driving scenarios.
Improving Recommendation Fairness via Data Augmentation
We augment imbalanced training data towards balanced data distribution to improve fairness.
Interactive Image Manipulation with Complex Text Instructions
To solve these problems, we propose a novel image manipulation method that interactively edits an image using complex text instructions.
Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification
Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world applications.
Gaia: Graph Neural Network with Temporal Shift aware Attention for Gross Merchandise Value Forecast in E-commerce
E-commerce has gone a long way in empowering merchants through the internet.
Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG
At the same time, the physics law between muscle forces and joint kinematics is used the soft constraint.
CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space
Session-based Recommendation (SBR) refers to the task of predicting the next item based on short-term user behaviors within an anonymous session.
Neural Graph Matching for Pre-training Graph Neural Networks
In this way, we can learn adaptive representations for a given graph when paired with different graphs, and both node- and graph-level characteristics are naturally considered in a single pre-training task.
An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022
Temporal link prediction, as one of the most crucial work in temporal graphs, has attracted lots of attention from the research area.
Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift
To this end, in this paper, we propose a novel Distribution Recovered Graph Self-Training framework (DR-GST), which could recover the distribution of the original labeled dataset.
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
As a result, client sampling plays an important role in FL systems as it affects the convergence rate of optimization algorithms used to train machine learning models.
Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation
Knowledge graph is generally incorporated into recommender systems to improve overall performance.
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data
Time series forecasting is widely used in business intelligence, e. g., forecast stock market price, sales, and help the analysis of data trend.
Real-time FPGA Design for OMP Targeting 8K Image Reconstruction
The choice of sensing matrix, the implementation of DP and LSP.
SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation
Specifically, we first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review.
Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure
Based on the tree structure, Thompson sampling is adapted with dynamic programming, leading to efficient exploration for potential ad creatives with the largest CTR.
Automated Creative Optimization for E-Commerce Advertising
However, interactions between creative elements may be more complex than the inner product, and the FM-estimated CTR may be of high variance due to limited feedback.
A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertising
Creative plays a great important role in e-commerce for exhibiting products.
LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign
In this paper, we analyze the network A/B testing problem under a real-world online marketing campaign, describe our proposed LinkLouvain method, and evaluate it on real-world data.
Heterogeneous Graph Neural Network for Recommendation
The prosperous development of e-commerce has spawned diverse recommendation systems.
Bandit Samplers for Training Graph Neural Networks
However, due to the intractable computation of optimal sampling distribution, these sampling algorithms are suboptimal for GCNs and are not applicable to more general graph neural networks (GNNs) where the message aggregator contains learned weights rather than fixed weights, such as Graph Attention Networks (GAT).
Ranked #1 on
Node Property Prediction
on ogbn-proteins
ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
Specifically, to associate the given target item with user behaviors over KG, we propose the graph connect and graph prune techniques to construct adaptive target-behavior relational graph.
A Natural Language Processing Pipeline of Chinese Free-text Radiology Reports for Liver Cancer Diagnosis
Despite the rapid development of natural language processing (NLP) implementation in electronic medical records (EMRs), Chinese EMRs processing remains challenging due to the limited corpus and specific grammatical characteristics, especially for radiology reports.
NetDP: An Industrial-Scale Distributed Network Representation Framework for Default Prediction in Ant Credit Pay
Considering the above challenges and the special scenario in Ant Financial, we try to incorporate default prediction with network information to alleviate the cold-start problem.
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing
In this paper, we present a graph representation learning method atop of transaction networks for merchant incentive optimization in mobile payment marketing.
DSSLP: A Distributed Framework for Semi-supervised Link Prediction
Instead of training model on the whole graph, DSSLP is proposed to train on the \emph{$k$-hops neighborhood} of nodes in a mini-batch setting, which helps reduce the scale of the input graph and distribute the training procedure.
Real-Time Semantic Segmentation via Multiply Spatial Fusion Network
Specifically, it achieves 77. 1% Mean IOU on the Cityscapes test dataset with the speed of 41 FPS for a 1024*2048 input, and 75. 4% Mean IOU with the speed of 91 FPS on the Camvid test dataset.
Progressive Feature Polishing Network for Salient Object Detection
Feature matters for salient object detection.
Spectrogram-frame linear network and continuous frame sequence for bird sound classification
Inspired by that bird sound has various frequency distributions and continuous time-varying properties, a novel method is proposed for the classification of bird sound based on continuous frame sequence and spectrogram-frame linear network (SFLN).
Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation
Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information.
Semantic Human Matting
SHM is the first algorithm that learns to jointly fit both semantic information and high quality details with deep networks.
Ranked #5 on
Image Matting
on AIM-500
Distributed Deep Forest and its Application to Automatic Detection of Cash-out Fraud
This model can block fraud transactions in a large amount of money each day.
Cascaded Pyramid Network for Multi-Person Pose Estimation
In this paper, we present a novel network structure called Cascaded Pyramid Network (CPN) which targets to relieve the problem from these "hard" keypoints.
Ranked #4 on
Keypoint Detection
on COCO test-challenge
Image Matters: Visually modeling user behaviors using Advanced Model Server
So we propose to model user preference jointly with user behavior ID features and behavior images.
